📄 Multi-File Multi-Run Screening Report

=== ABSTRACT ===
Introduction The use and availability of heated tobacco products has increased globally. However, the health effects of IQOS product use remain contradictory, and the impact of IQOS product use on respiratory health and immune biomarkers is lacking. Industry sponsored studies suggest IQOS products reduce exposure to harmful chemicals compared to conventional cigarettes and show attenuated inflammatory responses compared to cigarette smoke, yet independent research studies indicate exposure to IQOS is still associated with negative health outcomes similar to conventional cigarettes. Clinical data is needed to understand the respiratory health impacts of IQOS use in people who currently smoke cigarettes. Methods Adults located in Los Angeles, California were recruited for the Adult IQOS Respiratory Study (AIRS, N = 32; 21-65 years). Nasal epithelial lining fluid (NELF) and saliva collected from adults who were willing to incorporate IQOS into their daily habits, were analyzed by multiplex enzyme linked immunosorbent assays for 29 different inflammatory markers or cotinine, respectively. Repeated measures ANOVA were used to identify changes in immune markers across the weekly visits. Linear mixed modelling was used to identify changes in participant cigarette or IQOS HeetStick use over the study period. Day was added as a fixed effect. Results Between-participants variability in IQOS or cigarette usage was identified (p < 0.0001). IQOS and cigarette use did not significantly change across time and remained stable over the course of the 4-week study period. No significant changes were identified in soluble protein mediators across each week. Cotinine concentrations remained consistent, even when disaggregated by sex and visit. Conclusions All together, these data highlight that dual product use may be more likely in individuals who smoke cigarettes, particularly over the first month of incorporation, rather than completely switching to IQOS products when provided. Furthermore, incorporating IQOS did not significantly change nicotine exposure or nasal inflammatory biomarkers, suggesting, at least with nicotine exposure and nasal inflammation, that addition of heated tobacco products may not substantially reduce harm. What is already known on this topic: Heated tobacco products are marketed as less harmful and smoke-free alternative products compared to conventional cigarettes; however, lack of respiratory data and contradictory reports suggest they may still pose risks and use may contribute to consequential health impacts. Short-term and industry sponsored studies are available, but limited clinical data exists on the respiratory and immunological impacts of IQOS product use. What this study adds: In the follow repeated measures study, our findings provide clinical evidence there are no significant differences in nasal immune biomarkers in individuals that smoke, who incorporate IQOS into their lifestyles. While each participant varied in their IQOS and cigarette product use, no significant trends were observed over the four-week sampling period in overall IQOS or cigarette use. Additionally, when participants were provided with the IQOS option, they were more inclined to dual use both products rather than switch from cigarettes to only IQOS use in the 4 week period studied. How this study might affect research, practice or policy: The following clinical study suggests there is an increased likelihood of dual use among individuals who are provided alternative tobacco products. Additional studies should consider evaluating health differences, including immunological markers, between individuals who dual use IQOS and other tobacco products, exclusively use IQOS, people who completely switch their product use and people who quit altogether to establish whether IQOS use reduces harmful exposures resulting in differential inflammatory responses when compared to cigarette use.


=== TITLE ===
IQOS product use in adults who smoke cigarettes daily does not change respiratory inflammatory markers


=== DISCUSSION ===

IQOS are becoming more readily available particularly in the United States, however knowledge of their impact on respiratory health is limited. In this preliminary pilot study, we investigated nasal cytokines from adults who smoke cigarettes daily, who were willing to incorporate IQOS products into their daily habits for 30 days. Overall, we discerned no significant differences in cytokine signatures or use patterns over the sampling period. Both IQOS and CC use remained relatively stable across time between participants, regardless of baseline use patterns. Likewise, nicotine exposure remained unchanged over the study period. To evaluate whether participants adopted IQOS products, we used LMM to evaluate trends in participants IQOS and CC use. We found between-participant variation to be significant as each participant had different average IQOS and CC use throughout the study. However, no significant changes in IQOS or CC use were found with time in the study.
IQOS aerosols were previously found to induce proinflammatory immune mediators, creating a proinflammatory microenvironment leading to lung damage like CS compared to air controls (12). Further, long-term exposure to IQOS aerosols produced upregulation of cell death and inflammatory genes resulting in apoptosis induced emphysema in murine lung (29). Limited research exists on dual use of IQOS and CC, however, in vitro studies suggest increased physiological and metabolic damage to airway epithelium compared to either product alone (30). Previous studies have noted similar salivary cotinine levels between people who exclusively use HTPs and individuals who use CCs. Similarly, IQOS and CC were found to yield nearly identical nicotine content and freebase nicotine under similar puffing regimens in other studies (31,32). These data suggest HTP use is accompanied with similar CC risks and nicotine consumption, increasing the likelihood of sustained or developing addiction, particularly among people who do not smoke adopting use of IQOS without prior nicotine use. The misleading nature of IQOS being marketed as "smokeless and smoke-free" cigarette alternatives increases consumer interest. Ghazi and colleagues reviewed available literature on the health impacts and toxicity of IQOS and compared independent researcher data to industry sponsored studies and noted discrepancies between both sources (33). Ultimately, they concluded IQOS may not always coincide with the reduced risk potential that is advertised compared to cigarettes.
In the only other prior CC to IQOS adoption study examining respiratory health endpoints, Bhat et al., (2024) showed switching from CC to IQOS did not result in significant improvements in lung inflammation or damage compared to CC smoke exposure in vivo (34). While our study does not do a side-by-side comparison, we observed that IQOS use did not result in any attenuation of inflammatory cytokines. Our results here corroborate similar findings in the field that indicate switching to or the addition of IQOS products does not improve health effects compared to sole CC use and are likely not as "reduced risk" as claimed. These results are important as individuals who are adding IQOS use likely will not experience improvements in respiratory immune profiles. Further, these results suggest that IQOS use likely induces similar respiratory immune alterations to CC use, therefore adoption by never users or former smokers should be discouraged.


=== INTRODUCTION ===

Conventional cigarettes (CC) have known consequences and risks of use and exposure including lung inflammation, impaired lung immunity (1), structural damage caused by genetic changes (2) and increased risk of respiratory diseases including chronic obstructive pulmonary disease (COPD) (3). Introduction of purported safer alternatives to the tobacco market has been progressively rising, including heated tobacco products (HTPs). HTPs, which were presented to the tobacco industry in 2014, are alternative cigarette products that avoid combustion by heating included tobacco below temperatures of CC (4). This allows for nicotine delivery without the carcinogens and chemicals produced by burned tobacco -creating a possibly less harmful product compared to CC. IQOS, a popular HTP available globally in over 60 countries, was authorized by FDA for sale in the US as a modified risk tobacco product (MRTP) in 2020 (5). This authorization allowed Philip Morris International (PMI), to claim their products reduce exposure risk to dangerous chemicals and products compared to cigarette smoke (CS). Studies have indicated attenuated inflammatory responses in epithelium cultures (6) and slowed progression of cardiovascular and respiratory changes in mice (7) compared to CS. Human-based studies indicated lower exposure levels to harmful and potentially harmful chemicals and sustained reductions in exposure biomarkers (8). However, these studies were conducted by PMI, and the human-based study focused on reduced outcomes in individuals who smoke cigarettes and were transitioning to HTP use. These studies failed to evaluate inflammatory and lung-specific immunological components of HTP use. It is currently unclear whether dual CC and HTP use, such as in the process of switching products, alters or improves respiratory outcomes over sole CC use in humans. Prior studies have primarily evaluated the effectiveness of switching, without evaluating changes in respiratory biology (9,10).
Industry independent research indicates HTPs still pose risks in healthy subjects and animal models, including induction of oxidative stress events and transcriptional changes in bronchial epithelial cells and alveolar mucosa models (11), increased proinflammatory biomarkers and compromised immune cell function in mouse lung (12) and increased cytotoxicity and inflammatory biomarkers compared to CS and e-cigarettes in vitro (13,14). These risks should especially be acknowledged as these products are not only being adopted by individuals who currently smoke cigarettes attempting to switch to a lower risk product or quit smoking. Studies have found increased adoption of HTPs among individuals with no history of smoking and those who previously smoked, including youth and young adults, (15,16). Altogether this raises the important question: what is the respiratory impact of IQOS product use? To answer this, we aimed to understand whether incorporation of IQOS products resulted in reduced CC use and how IQOS product use impacted respiratory inflammation. The following study reports on the respiratory impact of IQOS use, from adults who smoke cigarettes daily, but were willing to try incorporating IQOS use for 30 days. Participants were not required to eliminate use of cigarettes during the study. We evaluated respiratory immune mediators and cotinine concentrations, from nasal epithelial lining fluid (NELF) and saliva samples, respectively, over a 4-week period.

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

Objective  
Confirm whether the manuscript qualifies as a research paper or a systematic review based on structural and methodological characteristics.

Instructions  
1. Evaluate the manuscript for the following:
 - A research paper presents original research findings based on experiments, observations, or analyses conducted by the authors.
 - A systematic review provides a structured review of prior research following a clear methodology (e.g., inclusion/exclusion criteria, data synthesis).
2. For systematic reviews, look for indicators such as:
 - Mention of search strategies, search terms, or databases used.
 - Use of established frameworks like PRISMA, PICO, or equivalent (optional but useful indicators).
3. Check for presence of standard sections:
 - Abstract, Introduction, Methods, Results, Discussion/Conclusion.
 - Accept variants such as Background, Materials and Methods, Findings, or Conclusions.
4. Look for language suggesting original research or systematic methodology, such as:
 - “we conducted experiments,” “our findings show,” “we systematically reviewed,” “studies were selected based on.”
5. Mark the manuscript as FAIL if it lacks structural and methodological indicators of original research or systematic review.
- Narrative summaries, essays, or theory pieces without structured methodology should be marked FAIL.
6.  If it IS a research paper or a systematic review, mark as PASS. If it is NOT a research paper or a systematic review, mark as FAIL.

Output Format
- PASS / FAIL
- Detailed Explanation
- Context From the Paper

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

**SPECIFIC CHECK:**
Objective  
Confirm whether the manuscript qualifies as a research paper or a systematic review based on structural and methodological characteristics.

Instructions  
1. Evaluate the manuscript for the following:
 - A research paper presents original research findings based on experiments, observations, or analyses conducted by the authors.
 - A systematic review provides a structured review of prior research following a clear methodology (e.g., inclusion/exclusion criteria, data synthesis).
2. For systematic reviews, look for indicators such as:
 - Mention of search strategies, search terms, or databases used.
 - Use of established frameworks like PRISMA, PICO, or equivalent (optional but useful indicators).
3. Check for presence of standard sections:
 - Abstract, Introduction, Methods, Results, Discussion/Conclusion.
 - Accept variants such as Background, Materials and Methods, Findings, or Conclusions.
4. Look for language suggesting original research or systematic methodology, such as:
 - “we conducted experiments,” “our findings show,” “we systematically reviewed,” “studies were selected based on.”
5. Mark the manuscript as FAIL if it lacks structural and methodological indicators of original research or systematic review.
- Narrative summaries, essays, or theory pieces without structured methodology should be marked FAIL.
6.  If it IS a research paper or a systematic review, mark as PASS. If it is NOT a research paper or a systematic review, mark as FAIL.

Output Format
- PASS / FAIL
- Detailed Explanation
- Context From the Paper

**DOCUMENT CONTENT TO ANALYZE:**

=== ABSTRACT ===
Introduction The use and availability of heated tobacco products has increased globally. However, the health effects of IQOS product use remain contradictory, and the impact of IQOS product use on respiratory health and immune biomarkers is lacking. Industry sponsored studies suggest IQOS products reduce exposure to harmful chemicals compared to conventional cigarettes and show attenuated inflammatory responses compared to cigarette smoke, yet independent research studies indicate exposure to IQOS is still associated with negative health outcomes similar to conventional cigarettes. Clinical data is needed to understand the respiratory health impacts of IQOS use in people who currently smoke cigarettes. Methods Adults located in Los Angeles, California were recruited for the Adult IQOS Respiratory Study (AIRS, N = 32; 21-65 years). Nasal epithelial lining fluid (NELF) and saliva collected from adults who were willing to incorporate IQOS into their daily habits, were analyzed by multiplex enzyme linked immunosorbent assays for 29 different inflammatory markers or cotinine, respectively. Repeated measures ANOVA were used to identify changes in immune markers across the weekly visits. Linear mixed modelling was used to identify changes in participant cigarette or IQOS HeetStick use over the study period. Day was added as a fixed effect. Results Between-participants variability in IQOS or cigarette usage was identified (p < 0.0001). IQOS and cigarette use did not significantly change across time and remained stable over the course of the 4-week study period. No significant changes were identified in soluble protein mediators across each week. Cotinine concentrations remained consistent, even when disaggregated by sex and visit. Conclusions All together, these data highlight that dual product use may be more likely in individuals who smoke cigarettes, particularly over the first month of incorporation, rather than completely switching to IQOS products when provided. Furthermore, incorporating IQOS did not significantly change nicotine exposure or nasal inflammatory biomarkers, suggesting, at least with nicotine exposure and nasal inflammation, that addition of heated tobacco products may not substantially reduce harm. What is already known on this topic: Heated tobacco products are marketed as less harmful and smoke-free alternative products compared to conventional cigarettes; however, lack of respiratory data and contradictory reports suggest they may still pose risks and use may contribute to consequential health impacts. Short-term and industry sponsored studies are available, but limited clinical data exists on the respiratory and immunological impacts of IQOS product use. What this study adds: In the follow repeated measures study, our findings provide clinical evidence there are no significant differences in nasal immune biomarkers in individuals that smoke, who incorporate IQOS into their lifestyles. While each participant varied in their IQOS and cigarette product use, no significant trends were observed over the four-week sampling period in overall IQOS or cigarette use. Additionally, when participants were provided with the IQOS option, they were more inclined to dual use both products rather than switch from cigarettes to only IQOS use in the 4 week period studied. How this study might affect research, practice or policy: The following clinical study suggests there is an increased likelihood of dual use among individuals who are provided alternative tobacco products. Additional studies should consider evaluating health differences, including immunological markers, between individuals who dual use IQOS and other tobacco products, exclusively use IQOS, people who completely switch their product use and people who quit altogether to establish whether IQOS use reduces harmful exposures resulting in differential inflammatory responses when compared to cigarette use.


=== TITLE ===
IQOS product use in adults who smoke cigarettes daily does not change respiratory inflammatory markers


=== DISCUSSION ===

IQOS are becoming more readily available particularly in the United States, however knowledge of their impact on respiratory health is limited. In this preliminary pilot study, we investigated nasal cytokines from adults who smoke cigarettes daily, who were willing to incorporate IQOS products into their daily habits for 30 days. Overall, we discerned no significant differences in cytokine signatures or use patterns over the sampling period. Both IQOS and CC use remained relatively stable across time between participants, regardless of baseline use patterns. Likewise, nicotine exposure remained unchanged over the study period. To evaluate whether participants adopted IQOS products, we used LMM to evaluate trends in participants IQOS and CC use. We found between-participant variation to be significant as each participant had different average IQOS and CC use throughout the study. However, no significant changes in IQOS or CC use were found with time in the study.
IQOS aerosols were previously found to induce proinflammatory immune mediators, creating a proinflammatory microenvironment leading to lung damage like CS compared to air controls (12). Further, long-term exposure to IQOS aerosols produced upregulation of cell death and inflammatory genes resulting in apoptosis induced emphysema in murine lung (29). Limited research exists on dual use of IQOS and CC, however, in vitro studies suggest increased physiological and metabolic damage to airway epithelium compared to either product alone (30). Previous studies have noted similar salivary cotinine levels between people who exclusively use HTPs and individuals who use CCs. Similarly, IQOS and CC were found to yield nearly identical nicotine content and freebase nicotine under similar puffing regimens in other studies (31,32). These data suggest HTP use is accompanied with similar CC risks and nicotine consumption, increasing the likelihood of sustained or developing addiction, particularly among people who do not smoke adopting use of IQOS without prior nicotine use. The misleading nature of IQOS being marketed as "smokeless and smoke-free" cigarette alternatives increases consumer interest. Ghazi and colleagues reviewed available literature on the health impacts and toxicity of IQOS and compared independent researcher data to industry sponsored studies and noted discrepancies between both sources (33). Ultimately, they concluded IQOS may not always coincide with the reduced risk potential that is advertised compared to cigarettes.
In the only other prior CC to IQOS adoption study examining respiratory health endpoints, Bhat et al., (2024) showed switching from CC to IQOS did not result in significant improvements in lung inflammation or damage compared to CC smoke exposure in vivo (34). While our study does not do a side-by-side comparison, we observed that IQOS use did not result in any attenuation of inflammatory cytokines. Our results here corroborate similar findings in the field that indicate switching to or the addition of IQOS products does not improve health effects compared to sole CC use and are likely not as "reduced risk" as claimed. These results are important as individuals who are adding IQOS use likely will not experience improvements in respiratory immune profiles. Further, these results suggest that IQOS use likely induces similar respiratory immune alterations to CC use, therefore adoption by never users or former smokers should be discouraged.


=== INTRODUCTION ===

Conventional cigarettes (CC) have known consequences and risks of use and exposure including lung inflammation, impaired lung immunity (1), structural damage caused by genetic changes (2) and increased risk of respiratory diseases including chronic obstructive pulmonary disease (COPD) (3). Introduction of purported safer alternatives to the tobacco market has been progressively rising, including heated tobacco products (HTPs). HTPs, which were presented to the tobacco industry in 2014, are alternative cigarette products that avoid combustion by heating included tobacco below temperatures of CC (4). This allows for nicotine delivery without the carcinogens and chemicals produced by burned tobacco -creating a possibly less harmful product compared to CC. IQOS, a popular HTP available globally in over 60 countries, was authorized by FDA for sale in the US as a modified risk tobacco product (MRTP) in 2020 (5). This authorization allowed Philip Morris International (PMI), to claim their products reduce exposure risk to dangerous chemicals and products compared to cigarette smoke (CS). Studies have indicated attenuated inflammatory responses in epithelium cultures (6) and slowed progression of cardiovascular and respiratory changes in mice (7) compared to CS. Human-based studies indicated lower exposure levels to harmful and potentially harmful chemicals and sustained reductions in exposure biomarkers (8). However, these studies were conducted by PMI, and the human-based study focused on reduced outcomes in individuals who smoke cigarettes and were transitioning to HTP use. These studies failed to evaluate inflammatory and lung-specific immunological components of HTP use. It is currently unclear whether dual CC and HTP use, such as in the process of switching products, alters or improves respiratory outcomes over sole CC use in humans. Prior studies have primarily evaluated the effectiveness of switching, without evaluating changes in respiratory biology (9,10).
Industry independent research indicates HTPs still pose risks in healthy subjects and animal models, including induction of oxidative stress events and transcriptional changes in bronchial epithelial cells and alveolar mucosa models (11), increased proinflammatory biomarkers and compromised immune cell function in mouse lung (12) and increased cytotoxicity and inflammatory biomarkers compared to CS and e-cigarettes in vitro (13,14). These risks should especially be acknowledged as these products are not only being adopted by individuals who currently smoke cigarettes attempting to switch to a lower risk product or quit smoking. Studies have found increased adoption of HTPs among individuals with no history of smoking and those who previously smoked, including youth and young adults, (15,16). Altogether this raises the important question: what is the respiratory impact of IQOS product use? To answer this, we aimed to understand whether incorporation of IQOS products resulted in reduced CC use and how IQOS product use impacted respiratory inflammation. The following study reports on the respiratory impact of IQOS use, from adults who smoke cigarettes daily, but were willing to try incorporating IQOS use for 30 days. Participants were not required to eliminate use of cigarettes during the study. We evaluated respiratory immune mediators and cotinine concentrations, from nasal epithelial lining fluid (NELF) and saliva samples, respectively, over a 4-week period.

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents an original experimental study with a defined cohort, methods, data analysis, and results, indicating it is a primary research paper rather than a systematic review.  
**CONTEXT:**  
- Abstract: “Adults located in Los Angeles, California were recruited for the Adult IQOS Respiratory Study (AIRS, N = 32; 21-65 years). … Repeated measures ANOVA were used to identify changes in immune markers across the weekly visits.”  
- Methods section: “Nasals epithelial lining fluid (NELF) and saliva collected … analyzed by multiplex enzyme linked immunosorbent assays for 29 different inflammatory markers or cotinine.”  
- Results: “Between-participants variability in IQOS or cigarette usage was identified (p < 0.0001). … No significant changes were identified in soluble protein mediators across each week.”  
- Discussion: “In this preliminary pilot study, we investigated nasal cytokines from adults who smoke cigarettes daily … Overall, we discerned no significant differences in cytokine signatures or use patterns over the sampling period.”

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents an original empirical study with a defined cohort, data collection procedures, statistical analyses, and reported results, indicating it is a research paper rather than a systematic review.  
**CONTEXT:**  
- “Adults located in Los Angeles, California were recruited for the Adult IQOS Respiratory Study (AIRS, N = 32; 21-65 years).”  
- “Nasal epithelial lining fluid (NELF) and saliva collected from adults … were analyzed by multiplex enzyme linked immunosorbent assays for 29 different inflammatory markers or cotinine.”  
- “Repeated measures ANOVA were used to identify changes in immune markers across the weekly visits.”  
- “Results Between-participants variability in IQOS or cigarette usage was identified (p < 0.0001).”  
- “No significant changes were identified in soluble protein mediators across each week.”  
These sections demonstrate original data collection and analysis, confirming the manuscript is a research paper.

=== DISCUSSION ===

IQOS are becoming more readily available particularly in the United States, however knowledge of their impact on respiratory health is limited. In this preliminary pilot study, we investigated nasal cytokines from adults who smoke cigarettes daily, who were willing to incorporate IQOS products into their daily habits for 30 days. Overall, we discerned no significant differences in cytokine signatures or use patterns over the sampling period. Both IQOS and CC use remained relatively stable across time between participants, regardless of baseline use patterns. Likewise, nicotine exposure remained unchanged over the study period. To evaluate whether participants adopted IQOS products, we used LMM to evaluate trends in participants IQOS and CC use. We found between-participant variation to be significant as each participant had different average IQOS and CC use throughout the study. However, no significant changes in IQOS or CC use were found with time in the study.
IQOS aerosols were previously found to induce proinflammatory immune mediators, creating a proinflammatory microenvironment leading to lung damage like CS compared to air controls (12). Further, long-term exposure to IQOS aerosols produced upregulation of cell death and inflammatory genes resulting in apoptosis induced emphysema in murine lung (29). Limited research exists on dual use of IQOS and CC, however, in vitro studies suggest increased physiological and metabolic damage to airway epithelium compared to either product alone (30). Previous studies have noted similar salivary cotinine levels between people who exclusively use HTPs and individuals who use CCs. Similarly, IQOS and CC were found to yield nearly identical nicotine content and freebase nicotine under similar puffing regimens in other studies (31,32). These data suggest HTP use is accompanied with similar CC risks and nicotine consumption, increasing the likelihood of sustained or developing addiction, particularly among people who do not smoke adopting use of IQOS without prior nicotine use. The misleading nature of IQOS being marketed as "smokeless and smoke-free" cigarette alternatives increases consumer interest. Ghazi and colleagues reviewed available literature on the health impacts and toxicity of IQOS and compared independent researcher data to industry sponsored studies and noted discrepancies between both sources (33). Ultimately, they concluded IQOS may not always coincide with the reduced risk potential that is advertised compared to cigarettes.
In the only other prior CC to IQOS adoption study examining respiratory health endpoints, Bhat et al., (2024) showed switching from CC to IQOS did not result in significant improvements in lung inflammation or damage compared to CC smoke exposure in vivo (34). While our study does not do a side-by-side comparison, we observed that IQOS use did not result in any attenuation of inflammatory cytokines. Our results here corroborate similar findings in the field that indicate switching to or the addition of IQOS products does not improve health effects compared to sole CC use and are likely not as "reduced risk" as claimed. These results are important as individuals who are adding IQOS use likely will not experience improvements in respiratory immune profiles. Further, these results suggest that IQOS use likely induces similar respiratory immune alterations to CC use, therefore adoption by never users or former smokers should be discouraged.


=== INTRODUCTION ===

Conventional cigarettes (CC) have known consequences and risks of use and exposure including lung inflammation, impaired lung immunity (1), structural damage caused by genetic changes (2) and increased risk of respiratory diseases including chronic obstructive pulmonary disease (COPD) (3). Introduction of purported safer alternatives to the tobacco market has been progressively rising, including heated tobacco products (HTPs). HTPs, which were presented to the tobacco industry in 2014, are alternative cigarette products that avoid combustion by heating included tobacco below temperatures of CC (4). This allows for nicotine delivery without the carcinogens and chemicals produced by burned tobacco -creating a possibly less harmful product compared to CC. IQOS, a popular HTP available globally in over 60 countries, was authorized by FDA for sale in the US as a modified risk tobacco product (MRTP) in 2020 (5). This authorization allowed Philip Morris International (PMI), to claim their products reduce exposure risk to dangerous chemicals and products compared to cigarette smoke (CS). Studies have indicated attenuated inflammatory responses in epithelium cultures (6) and slowed progression of cardiovascular and respiratory changes in mice (7) compared to CS. Human-based studies indicated lower exposure levels to harmful and potentially harmful chemicals and sustained reductions in exposure biomarkers (8). However, these studies were conducted by PMI, and the human-based study focused on reduced outcomes in individuals who smoke cigarettes and were transitioning to HTP use. These studies failed to evaluate inflammatory and lung-specific immunological components of HTP use. It is currently unclear whether dual CC and HTP use, such as in the process of switching products, alters or improves respiratory outcomes over sole CC use in humans. Prior studies have primarily evaluated the effectiveness of switching, without evaluating changes in respiratory biology (9,10).
Industry independent research indicates HTPs still pose risks in healthy subjects and animal models, including induction of oxidative stress events and transcriptional changes in bronchial epithelial cells and alveolar mucosa models (11), increased proinflammatory biomarkers and compromised immune cell function in mouse lung (12) and increased cytotoxicity and inflammatory biomarkers compared to CS and e-cigarettes in vitro (13,14). These risks should especially be acknowledged as these products are not only being adopted by individuals who currently smoke cigarettes attempting to switch to a lower risk product or quit smoking. Studies have found increased adoption of HTPs among individuals with no history of smoking and those who previously smoked, including youth and young adults, (15,16). Altogether this raises the important question: what is the respiratory impact of IQOS product use? To answer this, we aimed to understand whether incorporation of IQOS products resulted in reduced CC use and how IQOS product use impacted respiratory inflammation. The following study reports on the respiratory impact of IQOS use, from adults who smoke cigarettes daily, but were willing to try incorporating IQOS use for 30 days. Participants were not required to eliminate use of cigarettes during the study. We evaluated respiratory immune mediators and cotinine concentrations, from nasal epithelial lining fluid (NELF) and saliva samples, respectively, over a 4-week period.


=== ABSTRACT ===
Introduction The use and availability of heated tobacco products has increased globally. However, the health effects of IQOS product use remain contradictory, and the impact of IQOS product use on respiratory health and immune biomarkers is lacking. Industry sponsored studies suggest IQOS products reduce exposure to harmful chemicals compared to conventional cigarettes and show attenuated inflammatory responses compared to cigarette smoke, yet independent research studies indicate exposure to IQOS is still associated with negative health outcomes similar to conventional cigarettes. Clinical data is needed to understand the respiratory health impacts of IQOS use in people who currently smoke cigarettes. Methods Adults located in Los Angeles, California were recruited for the Adult IQOS Respiratory Study (AIRS, N = 32; 21-65 years). Nasal epithelial lining fluid (NELF) and saliva collected from adults who were willing to incorporate IQOS into their daily habits, were analyzed by multiplex enzyme linked immunosorbent assays for 29 different inflammatory markers or cotinine, respectively. Repeated measures ANOVA were used to identify changes in immune markers across the weekly visits. Linear mixed modelling was used to identify changes in participant cigarette or IQOS HeetStick use over the study period. Day was added as a fixed effect. Results Between-participants variability in IQOS or cigarette usage was identified (p < 0.0001). IQOS and cigarette use did not significantly change across time and remained stable over the course of the 4-week study period. No significant changes were identified in soluble protein mediators across each week. Cotinine concentrations remained consistent, even when disaggregated by sex and visit. Conclusions All together, these data highlight that dual product use may be more likely in individuals who smoke cigarettes, particularly over the first month of incorporation, rather than completely switching to IQOS products when provided. Furthermore, incorporating IQOS did not significantly change nicotine exposure or nasal inflammatory biomarkers, suggesting, at least with nicotine exposure and nasal inflammation, that addition of heated tobacco products may not substantially reduce harm. What is already known on this topic: Heated tobacco products are marketed as less harmful and smoke-free alternative products compared to conventional cigarettes; however, lack of respiratory data and contradictory reports suggest they may still pose risks and use may contribute to consequential health impacts. Short-term and industry sponsored studies are available, but limited clinical data exists on the respiratory and immunological impacts of IQOS product use. What this study adds: In the follow repeated measures study, our findings provide clinical evidence there are no significant differences in nasal immune biomarkers in individuals that smoke, who incorporate IQOS into their lifestyles. While each participant varied in their IQOS and cigarette product use, no significant trends were observed over the four-week sampling period in overall IQOS or cigarette use. Additionally, when participants were provided with the IQOS option, they were more inclined to dual use both products rather than switch from cigarettes to only IQOS use in the 4 week period studied. How this study might affect research, practice or policy: The following clinical study suggests there is an increased likelihood of dual use among individuals who are provided alternative tobacco products. Additional studies should consider evaluating health differences, including immunological markers, between individuals who dual use IQOS and other tobacco products, exclusively use IQOS, people who completely switch their product use and people who quit altogether to establish whether IQOS use reduces harmful exposures resulting in differential inflammatory responses when compared to cigarette use.


=== TITLE ===
IQOS product use in adults who smoke cigarettes daily does not change respiratory inflammatory markers

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

Objective  
Confirm whether the manuscript includes new data or qualifies as a clinical research protocol, based on content required by medRxiv.

Instructions 
1. Analyze the manuscript for the presence of either:
	- New data from experiments, observations, clinical trials, or simulations, even if based on previously published sources (e.g., meta-analyses with new synthesis).
	- For a medRxiv submission, check whether the submission is a clinical research protocol, defined as a structured plan for a clinical study.
2. Treat meta-analyses and data syntheses from prior studies as valid forms of ""data"" for this check, if they involve original re-analysis or synthesis.
3. To confirm presence of new data:
	- Review tables, figures, or data presentations that are not directly reused from prior published work.
	- Look for explicit mentions of new findings or original analysis.
4. To confirm a clinical research protocol:
	- Check for structured information on study objectives, design, methods, eligibility criteria, or trial phases.
	- Trial registration numbers or ethics review statements are useful indicators.
5. Mark the manuscript as FAIL if neither new data nor clinical protocol indicators are found.
	- If the content is ambiguous or borderline, mark as FAIL with a clear explanation so that a human screener can review the context.

Output
1. Provide a PASS or FAIL result
	- Has New Data Or Is A Clinical Research Protocol: PASS
	- Does Not Have New Data Or A Clinical Research Protocol: FAIL
2. Provide a Detailed Explanation to evidence the PASS or FAIL result
3. Provide the context From the Paper to evidence the PASS or FAIL result

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

**SPECIFIC CHECK:**
Objective  
Confirm whether the manuscript includes new data or qualifies as a clinical research protocol, based on content required by medRxiv.

Instructions 
1. Analyze the manuscript for the presence of either:
	- New data from experiments, observations, clinical trials, or simulations, even if based on previously published sources (e.g., meta-analyses with new synthesis).
	- For a medRxiv submission, check whether the submission is a clinical research protocol, defined as a structured plan for a clinical study.
2. Treat meta-analyses and data syntheses from prior studies as valid forms of ""data"" for this check, if they involve original re-analysis or synthesis.
3. To confirm presence of new data:
	- Review tables, figures, or data presentations that are not directly reused from prior published work.
	- Look for explicit mentions of new findings or original analysis.
4. To confirm a clinical research protocol:
	- Check for structured information on study objectives, design, methods, eligibility criteria, or trial phases.
	- Trial registration numbers or ethics review statements are useful indicators.
5. Mark the manuscript as FAIL if neither new data nor clinical protocol indicators are found.
	- If the content is ambiguous or borderline, mark as FAIL with a clear explanation so that a human screener can review the context.

Output
1. Provide a PASS or FAIL result
	- Has New Data Or Is A Clinical Research Protocol: PASS
	- Does Not Have New Data Or A Clinical Research Protocol: FAIL
2. Provide a Detailed Explanation to evidence the PASS or FAIL result
3. Provide the context From the Paper to evidence the PASS or FAIL result



**DOCUMENT CONTENT TO ANALYZE:**

=== DISCUSSION ===

IQOS are becoming more readily available particularly in the United States, however knowledge of their impact on respiratory health is limited. In this preliminary pilot study, we investigated nasal cytokines from adults who smoke cigarettes daily, who were willing to incorporate IQOS products into their daily habits for 30 days. Overall, we discerned no significant differences in cytokine signatures or use patterns over the sampling period. Both IQOS and CC use remained relatively stable across time between participants, regardless of baseline use patterns. Likewise, nicotine exposure remained unchanged over the study period. To evaluate whether participants adopted IQOS products, we used LMM to evaluate trends in participants IQOS and CC use. We found between-participant variation to be significant as each participant had different average IQOS and CC use throughout the study. However, no significant changes in IQOS or CC use were found with time in the study.
IQOS aerosols were previously found to induce proinflammatory immune mediators, creating a proinflammatory microenvironment leading to lung damage like CS compared to air controls (12). Further, long-term exposure to IQOS aerosols produced upregulation of cell death and inflammatory genes resulting in apoptosis induced emphysema in murine lung (29). Limited research exists on dual use of IQOS and CC, however, in vitro studies suggest increased physiological and metabolic damage to airway epithelium compared to either product alone (30). Previous studies have noted similar salivary cotinine levels between people who exclusively use HTPs and individuals who use CCs. Similarly, IQOS and CC were found to yield nearly identical nicotine content and freebase nicotine under similar puffing regimens in other studies (31,32). These data suggest HTP use is accompanied with similar CC risks and nicotine consumption, increasing the likelihood of sustained or developing addiction, particularly among people who do not smoke adopting use of IQOS without prior nicotine use. The misleading nature of IQOS being marketed as "smokeless and smoke-free" cigarette alternatives increases consumer interest. Ghazi and colleagues reviewed available literature on the health impacts and toxicity of IQOS and compared independent researcher data to industry sponsored studies and noted discrepancies between both sources (33). Ultimately, they concluded IQOS may not always coincide with the reduced risk potential that is advertised compared to cigarettes.
In the only other prior CC to IQOS adoption study examining respiratory health endpoints, Bhat et al., (2024) showed switching from CC to IQOS did not result in significant improvements in lung inflammation or damage compared to CC smoke exposure in vivo (34). While our study does not do a side-by-side comparison, we observed that IQOS use did not result in any attenuation of inflammatory cytokines. Our results here corroborate similar findings in the field that indicate switching to or the addition of IQOS products does not improve health effects compared to sole CC use and are likely not as "reduced risk" as claimed. These results are important as individuals who are adding IQOS use likely will not experience improvements in respiratory immune profiles. Further, these results suggest that IQOS use likely induces similar respiratory immune alterations to CC use, therefore adoption by never users or former smokers should be discouraged.


=== INTRODUCTION ===

Conventional cigarettes (CC) have known consequences and risks of use and exposure including lung inflammation, impaired lung immunity (1), structural damage caused by genetic changes (2) and increased risk of respiratory diseases including chronic obstructive pulmonary disease (COPD) (3). Introduction of purported safer alternatives to the tobacco market has been progressively rising, including heated tobacco products (HTPs). HTPs, which were presented to the tobacco industry in 2014, are alternative cigarette products that avoid combustion by heating included tobacco below temperatures of CC (4). This allows for nicotine delivery without the carcinogens and chemicals produced by burned tobacco -creating a possibly less harmful product compared to CC. IQOS, a popular HTP available globally in over 60 countries, was authorized by FDA for sale in the US as a modified risk tobacco product (MRTP) in 2020 (5). This authorization allowed Philip Morris International (PMI), to claim their products reduce exposure risk to dangerous chemicals and products compared to cigarette smoke (CS). Studies have indicated attenuated inflammatory responses in epithelium cultures (6) and slowed progression of cardiovascular and respiratory changes in mice (7) compared to CS. Human-based studies indicated lower exposure levels to harmful and potentially harmful chemicals and sustained reductions in exposure biomarkers (8). However, these studies were conducted by PMI, and the human-based study focused on reduced outcomes in individuals who smoke cigarettes and were transitioning to HTP use. These studies failed to evaluate inflammatory and lung-specific immunological components of HTP use. It is currently unclear whether dual CC and HTP use, such as in the process of switching products, alters or improves respiratory outcomes over sole CC use in humans. Prior studies have primarily evaluated the effectiveness of switching, without evaluating changes in respiratory biology (9,10).
Industry independent research indicates HTPs still pose risks in healthy subjects and animal models, including induction of oxidative stress events and transcriptional changes in bronchial epithelial cells and alveolar mucosa models (11), increased proinflammatory biomarkers and compromised immune cell function in mouse lung (12) and increased cytotoxicity and inflammatory biomarkers compared to CS and e-cigarettes in vitro (13,14). These risks should especially be acknowledged as these products are not only being adopted by individuals who currently smoke cigarettes attempting to switch to a lower risk product or quit smoking. Studies have found increased adoption of HTPs among individuals with no history of smoking and those who previously smoked, including youth and young adults, (15,16). Altogether this raises the important question: what is the respiratory impact of IQOS product use? To answer this, we aimed to understand whether incorporation of IQOS products resulted in reduced CC use and how IQOS product use impacted respiratory inflammation. The following study reports on the respiratory impact of IQOS use, from adults who smoke cigarettes daily, but were willing to try incorporating IQOS use for 30 days. Participants were not required to eliminate use of cigarettes during the study. We evaluated respiratory immune mediators and cotinine concentrations, from nasal epithelial lining fluid (NELF) and saliva samples, respectively, over a 4-week period.


=== ABSTRACT ===
Introduction The use and availability of heated tobacco products has increased globally. However, the health effects of IQOS product use remain contradictory, and the impact of IQOS product use on respiratory health and immune biomarkers is lacking. Industry sponsored studies suggest IQOS products reduce exposure to harmful chemicals compared to conventional cigarettes and show attenuated inflammatory responses compared to cigarette smoke, yet independent research studies indicate exposure to IQOS is still associated with negative health outcomes similar to conventional cigarettes. Clinical data is needed to understand the respiratory health impacts of IQOS use in people who currently smoke cigarettes. Methods Adults located in Los Angeles, California were recruited for the Adult IQOS Respiratory Study (AIRS, N = 32; 21-65 years). Nasal epithelial lining fluid (NELF) and saliva collected from adults who were willing to incorporate IQOS into their daily habits, were analyzed by multiplex enzyme linked immunosorbent assays for 29 different inflammatory markers or cotinine, respectively. Repeated measures ANOVA were used to identify changes in immune markers across the weekly visits. Linear mixed modelling was used to identify changes in participant cigarette or IQOS HeetStick use over the study period. Day was added as a fixed effect. Results Between-participants variability in IQOS or cigarette usage was identified (p < 0.0001). IQOS and cigarette use did not significantly change across time and remained stable over the course of the 4-week study period. No significant changes were identified in soluble protein mediators across each week. Cotinine concentrations remained consistent, even when disaggregated by sex and visit. Conclusions All together, these data highlight that dual product use may be more likely in individuals who smoke cigarettes, particularly over the first month of incorporation, rather than completely switching to IQOS products when provided. Furthermore, incorporating IQOS did not significantly change nicotine exposure or nasal inflammatory biomarkers, suggesting, at least with nicotine exposure and nasal inflammation, that addition of heated tobacco products may not substantially reduce harm. What is already known on this topic: Heated tobacco products are marketed as less harmful and smoke-free alternative products compared to conventional cigarettes; however, lack of respiratory data and contradictory reports suggest they may still pose risks and use may contribute to consequential health impacts. Short-term and industry sponsored studies are available, but limited clinical data exists on the respiratory and immunological impacts of IQOS product use. What this study adds: In the follow repeated measures study, our findings provide clinical evidence there are no significant differences in nasal immune biomarkers in individuals that smoke, who incorporate IQOS into their lifestyles. While each participant varied in their IQOS and cigarette product use, no significant trends were observed over the four-week sampling period in overall IQOS or cigarette use. Additionally, when participants were provided with the IQOS option, they were more inclined to dual use both products rather than switch from cigarettes to only IQOS use in the 4 week period studied. How this study might affect research, practice or policy: The following clinical study suggests there is an increased likelihood of dual use among individuals who are provided alternative tobacco products. Additional studies should consider evaluating health differences, including immunological markers, between individuals who dual use IQOS and other tobacco products, exclusively use IQOS, people who completely switch their product use and people who quit altogether to establish whether IQOS use reduces harmful exposures resulting in differential inflammatory responses when compared to cigarette use.


=== TITLE ===
IQOS product use in adults who smoke cigarettes daily does not change respiratory inflammatory markers

**RESULT:** PASS  
**EXPLANATION:** The manuscript reports original, prospective data from a 4‑week clinical study (Adult IQOS Respiratory Study, AIRS) involving 32 participants who were monitored for nasal inflammatory markers and nicotine exposure. The methods, statistical analyses, and reported results constitute new experimental data, satisfying medRxiv’s requirement for new data or a clinical research protocol.  
**CONTEXT:** “Adults located in Los Angeles, California were recruited for the Adult IQOS Respiratory Study (AIRS, N = 32; 21‑65 years). Nasal epithelial lining fluid (NELF) and saliva collected from adults who were willing to incorporate IQOS into their daily habits, were analyzed by multiplex enzyme linked immunosorbent assays for 29 different inflammatory markers or cotinine, respectively. Repeated measures ANOVA were used to identify changes in immune markers across the weekly visits. Linear mixed modelling was used to identify changes in participant cigarette or IQOS HeetStick use over the study period.”

**RESULT:** PASS  
**EXPLANATION:** The manuscript reports original experimental data from a prospective clinical study (Adult IQOS Respiratory Study) involving 32 participants, including collection of nasal epithelial lining fluid and saliva, measurement of 29 inflammatory markers, and statistical analysis of changes over a 4‑week period. This constitutes new data, satisfying medRxiv’s requirement.  
**CONTEXT:** “Adults located in Los Angeles, California were recruited for the Adult IQOS Respiratory Study (AIRS, N = 32; 21‑65 years). Nasal epithelial lining fluid (NELF) and saliva collected from adults who were willing to incorporate IQOS into their daily habits, were analyzed by multiplex enzyme linked immunosorbent assays for 29 different inflammatory markers or cotinine, respectively.” (Abstract – Methods) and “In this preliminary pilot study, we investigated nasal cytokines from adults who smoke cigarettes daily, who were willing to incorporate IQOS products into their daily habits for 30 days.” (Discussion).

=== ABSTRACT ===
Importance: Pain and mesh exposure in polypropylene mesh augmented pelvic organ prolapse repairs are linked to stiffness mismatches and mesh deformations (pore collapse and wrinkling). To overcome these limitations, we are developing novel elastomeric membranes (EM) that are macroporous and fabricated from a material that is softer than polypropylene (e.g. polycarbonate urethane) and more closely matches the stiffness of the vagina. Objective: This study assessed how pore geometry (auxetic-bowtie vs non-auxetic square or diamond) and material distribution impact the elongation, wrinkling, and porosity of EMs in response to tensile loads using finite element (FE) simulations. Study design: Nine models with varied strut dimensions were designed, maintaining constant total volume, device length, and device width. FE analysis based on a Neo-Hookean material model was used to apply 15 N uniaxial tensile loads in flat clamped and suture-like configurations. Results: For both configurations, diamond-pore membranes showed greatest elongation and porosity loss and square pore membranes showed the least. The suture-like configurations caused wrinkling that was most pronounced in bowtie-pore membranes, especially near attachment points, and least in square-pore membranes. The elongation of bowtie-pore membranes was most sensitive to material distribution, which also directly corresponded to the degree of wrinkling. Conclusion: Square pore geometry offered superior stability in response to uniaxial tension. Auxetic bowtie models showed porosity advantages, but material distribution impacted its elongation and corresponding propensity to wrinkle. These findings are critical for optimizing membrane design to minimize complications. Why this matters? (200/200 words) Current surgical meshes for pelvic organ prolapse (POP) repair remain largely adapted from hernia meshes, despite profound biomechanical and anatomical differences between the abdominal wall and the vagina. These devices are limited by their high stiffness and tendency for pore collapse and wrinkling when loaded which contributes directly to complications like chronic pain and mesh exposure. Improvements in prolapse-specific designs have not eliminated these issues, as the fundamental knitted polypropylene design remains restrictive. To address this unmet need, our work explores a vagina-centric solution-an elastomeric membrane (EM) manufactured from a material softer and more compliant than polypropylene, engineered with macropores to maintain porosity while minimizing wrinkling. Using finite element simulations, we explore the pore geometry and material distribution as key determinants of device stability. Square pore geometries consistently provided superior resistance to wrinkling and pore collapse under tensile loading, while diamond pores were highly unstable and auxetic bowtie pores, though resistant to collapse, exhibited pronounced wrinkling. These findings align with prior in vivo and ex vivo studies, underscoring the translational relevance. Importantly, this work establishes a cost-efficient computational framework for guiding next-generation device design, enabling safer, more effective prolapse repairs and providing surgeons with rational criteria for device selection.


=== TITLE ===
In silico Evaluation of an Elastomeric Membrane for Prolapse Repairs


=== DISCUSSION ===

Deformations like wrinkling and pore collapse contribute to complications observed when an polypropylene mesh is placed on tension during and following repairs of pelvic organ prolapse 15 (ie with standing, cough, sneeze etc). To overcome these behaviors, we have developed a novel softer device designed to conform to varied vaginal geometries with the goal of also minimizing deformations that have been shown to be consistent with complications. Our 3D printed porous elastomeric membrane is manufactured from PCU. In this study, computational modeling was used to inform an optimal pore geometry for the final iterations of the device. We asked how changing pore geometry (auxetic vs non-auxetic) and the material distribution within the strut width and device thickness impacted wrinkling behavior, displacement, and porosity of membranes. Pore geometries were either auxetic (bowtie) or non-auxetic (square and diamond) and models were created with narrow struts and a deep device depth, medium strut width and device depth, or wide struts and a shallow device depth. We looked at two independent boundary conditions, one in which both ends of the membranes were clamped to maintain a relatively flat configuration and a second, more clinically relevant condition in which sutures were simulated at opposing ends to mimic sewing to the sacrum and vagina. Regarding the primary outcome, bowtie models wrinkled the most followed by the diamond models. The square models, in contrast, were very stable remaining relatively flat (i.e., the square models did not wrinkle) in all conditions. Additionally, altering the strut width and device depth impacted the wrinkling behavior of the bowtie models the most with the Bowtie narrow-deep model wrinkling more than the Bowtie mid-mid and wideshallow models. Changing the strut width and device depth had minimal impact on the propensity of the diamond models to wrinkle, as unique wrinkling patterns were observed for all material distributions. Congruent with the auxetic design, the bowtie models experienced the greatest increase in porosity with loading while the greatest decrease in porosity (i.e., pore collapse) was observed for the diamond models. As anticipated, the pores of the square models remained open with very little increase in porosity in response to loading. Across all model combinations, the diamond models were the most unstable displacing the greatest amount as compared to the square models which displaced the least. Collectively, these results demonstrate that the gains in porosity of adopting an auxetic pore geometry are offset by its increasing propensity to wrinkle. On the other hand, while the porosity gains of the square pore geometry were not as significant, the pores did become larger, and that in combination with the minimal wrinkling and elongation indicated that it is the most favorable pore geometry a tensile loading application of this product. Of course, rotating a square pore by 45 degrees with respect to the direction of tension results in a diamond geometry, so it is critical that the alignment of the pores with respect to the direction of tension is considered.
Wrinkling of thin films/membranes stems from the inability of the film/membrane to withstand lateral compressive stresses when being pulled longitudinally, and that ultimately results in out-of-plane deformation (i.e., wrinkling) 29 . Thus, it was thought that the lateral expansion that results from the auxetic pore geometry would serve to flatten the device as has been shown previously 30 ; however, it seems to have served to destabilize it with respect to out-of-plane deformations in this study, which resulted in the wrinkling observed for auxetic bowtie models that was contrary to the study hypothesis. The increased propensity of the bowtie models to wrinkle may be related to the size of the unit cells (pores) with respect to the membrane dimensions and a result that is specific to a macroporous design. 31,32 Another interesting finding was that wide-shallow membranes tended to be more stable than narrow-deep membranes. Based on wrinkling theory of continuous materials, it is anticipated that the opposite would be true. However, it should be noted that this finding is likely to change if we had used a fixed displacement (i.e., all membranes loaded until they had displaced the same distance) which is less typical of in vivo surgical loading conditions. The results of this study showed that material distribution resulted in changes in elongation for auxetic and diamond membranes, with less elongation for wide-shallow devices being due to having more material distributed in a way to prevent pore deformation. In addition to the depth of the device, wrinkling theory also shows that longitudinal deformation is also a major factor in wrinkling. Therefore, while making a device shallower will increase the propensity for it to wrinkle, strategically redistributing that material to prevent elongation of the membrane can serve to limit that increased propensity.
Lastly, in an ex vivo mechanical study performed by Barone et al 2015, a square pore prolapse mesh was shown to wrinkle a lot more relative to the square models in the current study 16 .
The discrepancy in the results may be contributed to the differences in manufacturing. The polypropylene meshes assessed by Barone et al 2015 were knitted (i.e., consisting of fibers and knots) whereas the models in the current study are essentially porous membranes (with struts and no knots) and are designed to be manufactured via 3D printing or modeling. The minimal wrinkling observed in the current study suggests that manufacturing devices without knitting may minimize wrinkling and reduce the risk of complications. Future ex vivo mechanical studies comparing the wrinkling behavior of knitted vs non-knitted devices are needed to assess the impact of manufacturing technique on wrinkling behavior. Ongoing studies utilizing ex vivo mechanical testing and quantitative methods are warranted to confirm these findings.
In this study, all models were designed with the same dimensions (length and width), the same pore area, and a constant mass. Additionally, the overall volume of the models was kept constant across all models by re-distributing the material gained or lost in the width of the strut (xand y-direction) to the thickness of the device (z-direction). Maintaining consistencies in the design of the models is a major strength of this study as it allowed for the isolation of the impact of changing strut dimensions and pore geometry on the outcomes of this study (i.e., wrinkling, porosity, and displacement). Additionally, utilizing the clamped configuration (i.e., BC 1) minimized wrinkling, thus elucidating how each pore geometry (i.e., bowtie, square, and diamond) influenced porosity. Lastly, finite element analysis (FEA) was a cost-effective and efficient method to assess the impact of various design parameters on device performance without having to manufacture and perform ex vivo mechanical testing. It is important to note that this is a computational study and, while that give us great control over study variables, some of the assumptions made will impact the ability to translate the absolute values determined in this study to the real world. In particular, the assumption of hyperelastic Neo-Hookean material model to represent PCU is an approximation. Nevertheless, while absolute values may differ for an actual PCU device, the trends and overall conclusions of this study are very likely to be applicable. It is also important for the reader to note when interpreting the results of this study is that one can design a device that is mechanically superior (i.e., meets all requirement from a mechanical behavior perspective) yet does not produce a favorable host response. To date, in vivo evidence exists that demonstrates square pore meshes are more advantageous than a diamond pore mesh and while that is likely to translate to membranes based on the finding of this study, the biologic response to material distribution that is independent from the mechanical behavior it imparts is still unknown.
Overall, this study demonstrates that the square pore geometry, as compared to the diamond and bowtie, is the most stable pore geometry in response to tensile loads as characterized by negligible wrinkling and a minimal change in pore shape, porosity, and displacement irrespective of material distribution. The diamond geometry on the other hand is unstable and likely more susceptible to complications. Meanwhile, the bowtie geometry is beneficial for enhancing porosity and future in vivo studies are warranted to determine whether the unique behavior can enhance outcomes, the unanticipated degree of wrinkling is a significant tradeoff. Ultimately, the results of this study inform the design of future devices for prolapse repairs that reduces the risk of complications. the strut width (top) and device depth (bottom). Note, pictures represent only a portion of the full-length models used for FEA. B. Uniaxial tensile tests were stimulated utilizing two boundary conditions. Boundary condition 1 (left), the top and bottom edges were fully clamped to simulate a typical uniaxial tensile experimental testing set-up. Boundary condition 2 (right), four boxes were attached to the top and bottom edges (two per edge) to simulate suture attachments. Arrows represent traction force applied. condition 1. Overall, the Square membranes displaced the least while the Diamond membranes displaced the most. condition 2. Note each top view is scaled to equal width for visualization. Displacement decreases with increasing strut width for Bowtie and Diamond membranes but remains stable across Square membranes. The wrinkling pattern is distinct between pore shapes.  membrane (i.e., from top to bottom). The bowtie models wrinkle more than the diamond or    Membrane Relative Porosity Change -Boundary Condition 1: Clamped Relative Porosity Change -Boundary Condition 2: Suture Bowtie Narrow-Deep 14.3% 12.3% Bowtie Mid-Mid 11.0% 10.4% Bowtie Wide-Shallow 9.3% 9.4% Diamond Narrow-Deep -8.2% -10.8% Diamond Mid-Mid -5.1% -7.2% Diamond Wide-Shallow -1.6% -3.1% Square Narrow-Deep 1.4% 0.6% Square Mid-Mid 1.9% 1.3% Square Wide-Shallow 2.6% 2.2% Table 1. Percent change in porosity for boundary conditions 1 and 2, relative to the undeformed configuration. , from top to bottom). The bowtie models wrinkle more than the diamond or square models. For this pore shape, the middle is the most curved whereas the regions near the suture attachments remain relatively flatter. Material distribution for the bowtie pores does not have a distinct effect. By contrast, the Diamond narrowdeep model exhibits the inverse shape where the most curved regions are near the attachment points. However, the Mid and Wide models show more more uniform behavior along the length than the narrow-deep model. The square pore models remarkably resist wrinkling where the Wide-Shallow model has a marked uniform increase compared to the narrow or mid models. This supports the conclusion that square pores remain flat where auxetic pores seem to drive wrinkling. Models that have a shallower depth exhibit more consistent behavior along the length when compared to models with a deep depth in the bowtie and diamond groups.


=== INTRODUCTION ===

Pelvic organ prolapse (POP) is a common costly condition in which the pelvic organs descend from their normal anatomic position in the pelvis into the vagina, and often through the vaginal opening. Women with symptomatic POP often experience a vaginal bulge and pressure, pain, sexual dysfunction, and dysfunction with voiding and defecation [1][2][3] . Approximately 13% of U.S.
women will undergo a surgery to repair POP in their lifetime, with surgical costs exceeding $1.5 billion annually 4,5 . By 2050, it is estimated that the number of women with POP will increase 50%, posing a significant health and economic burden on society 6 .
Surgeons utilize knitted synthetic meshes to reinforce POP repairs. Originally designed for hernia repairs, synthetic meshes used in POP repairs are associated with complications, most notably pain and exposure of mesh fibers through the vaginal epithelium (mesh exposure). These complications occur in roughly 10% of cases, and this rate increases with time [7][8][9][10] . Research suggests that stiffness mismatches between the mesh and the vagina and mesh deformation (pore collapse and wrinkling) with tensioning contribute to mesh complications [11][12][13][14][15] . Specifically, studies implanting synthetic meshes with varying stiffnesses onto the vagina of nonhuman primates have demonstrated that meshes with high structural stiffness are associated with a maladaptive remodeling response that results in vaginal degeneration indicative of stress shielding [11][12][13][14] . Ex vivo mechanical testing and computational modeling studies have shown that the pores of most synthetic meshes tend to collapse and the meshes wrinkle in response to tension [16][17][18][19] . Indeed, implanting meshes with collapsed pores and wrinkles onto the nonhuman primate vagina effectively reproduced complications with mesh exposures, loss of vaginal rugae, and vaginal thinning consistent with what is observed clinically 15 . Collectively these results highlight an emergent need for an alternative device for POP repairs that is made from a soft material, maintains porosity (i.e., the pores do not collapse), and minimizes wrinkling when tensioned.
To address this need, we are developing novel elastomeric membranes. Previously, we highlighted that changing the pores to an auxetic geometry obviates pore collapse 20 . Auxetic pore geometries have a negative Poisson's ratio; that is, unlike typical everyday materials, they expand in the transverse direction when stretched/tensioned longitudinally. We refer to our devices as membranes, as opposed to meshes, since they are a continuous piece of material with macroscopic pores. Meshes, on the other hand, are composed of multiple thin fibers (often made from polypropylene) that are knitted. Knitting is necessary because a continuous piece of polypropylene, even with macroscopic pores punched out, would result in a device with very little compliance, similar to that of a Tupperware TM container. Since our membrane is manufactured from polycarbonate urethane (PCU), a polymer that is substantially softer than polypropylene and can be tuned to match the material properties of the vagina by varying its hard and soft components, it allows for a macroporous membrane design. Additionally, given our device is not knitted with fibers, we refer to the regions of material bounding the pores as struts (as opposed to fibers). We have shown that membranes developed from PCU have a similar failure load and fatigue resistance to a commercially available polypropylene mesh (Restorelle, Coloplast, Coloplast, Minneapolis, MN, USA) 21 . PCU is an elastomer and as such, is less susceptible to permanent deformation with loading. At the same time, its toughness exceeds that of most elastomers.
Finite element analysis is a computational modeling framework that allows for controlled and cost-effective simulations of POP repair devices exposed to loading conditions that mimic surgical repair prior to or as a supplement to implantation. In the current study, finite element analysis was used to inform how design parameters, specifically pore shape (auxetic vs non-auxetic) and material distribution (dimensions of the struts), impact the propensity of the PCU membranes to wrinkle (nonplanar deformation), since a final design associated with the least amount of wrinkling is a highly desirable endpoint. As a secondary outcome, the impact of design parameters on elongation (change in length with tension) and porosity were also assessed. Based on previous studies, we examined the "bowtie" shape as our auxetic pore geometry (selected based on a previous computational study) 20 and used diamond and square shapes as non-auxetic geometries since the pores of current polypropylene meshes are typically square or diamond shaped. Two different loading configurations were examined in our finite element simulations. In the first, typical of uniaxial tensile tests that are often used to evaluate the mechanical behavior of POP devices, the devices (15 x 5 cm) were tensioned by simulating clamps affixed to both of its shorter ends such that tension could be applied along the long axis of the device. For the second loading configuration, the clamps were removed and tension was applied to two small areas on each of the opposing ends of the device to simulate sutures, a scenario that more closely mimics tension applied during surgery. The boundary conditions of the second loading configuration are less constrained and likely to reveal mechanical instabilities that favor wrinkling. We hypothesized that the auxetic behavior would have the lowest amount of wrinkling and highest porosity and that membranes that were thinner in depth would have an increased propensity to wrinkle.

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

Objective  
Confirm whether the manuscript qualifies as a research paper or a systematic review based on structural and methodological characteristics.

Instructions  
1. Evaluate the manuscript for the following:
 - A research paper presents original research findings based on experiments, observations, or analyses conducted by the authors.
 - A systematic review provides a structured review of prior research following a clear methodology (e.g., inclusion/exclusion criteria, data synthesis).
2. For systematic reviews, look for indicators such as:
 - Mention of search strategies, search terms, or databases used.
 - Use of established frameworks like PRISMA, PICO, or equivalent (optional but useful indicators).
3. Check for presence of standard sections:
 - Abstract, Introduction, Methods, Results, Discussion/Conclusion.
 - Accept variants such as Background, Materials and Methods, Findings, or Conclusions.
4. Look for language suggesting original research or systematic methodology, such as:
 - “we conducted experiments,” “our findings show,” “we systematically reviewed,” “studies were selected based on.”
5. Mark the manuscript as FAIL if it lacks structural and methodological indicators of original research or systematic review.
- Narrative summaries, essays, or theory pieces without structured methodology should be marked FAIL.
6.  If it IS a research paper or a systematic review, mark as PASS. If it is NOT a research paper or a systematic review, mark as FAIL.

Output Format
- PASS / FAIL
- Detailed Explanation
- Context From the Paper

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

**SPECIFIC CHECK:**
Objective  
Confirm whether the manuscript qualifies as a research paper or a systematic review based on structural and methodological characteristics.

Instructions  
1. Evaluate the manuscript for the following:
 - A research paper presents original research findings based on experiments, observations, or analyses conducted by the authors.
 - A systematic review provides a structured review of prior research following a clear methodology (e.g., inclusion/exclusion criteria, data synthesis).
2. For systematic reviews, look for indicators such as:
 - Mention of search strategies, search terms, or databases used.
 - Use of established frameworks like PRISMA, PICO, or equivalent (optional but useful indicators).
3. Check for presence of standard sections:
 - Abstract, Introduction, Methods, Results, Discussion/Conclusion.
 - Accept variants such as Background, Materials and Methods, Findings, or Conclusions.
4. Look for language suggesting original research or systematic methodology, such as:
 - “we conducted experiments,” “our findings show,” “we systematically reviewed,” “studies were selected based on.”
5. Mark the manuscript as FAIL if it lacks structural and methodological indicators of original research or systematic review.
- Narrative summaries, essays, or theory pieces without structured methodology should be marked FAIL.
6.  If it IS a research paper or a systematic review, mark as PASS. If it is NOT a research paper or a systematic review, mark as FAIL.

Output Format
- PASS / FAIL
- Detailed Explanation
- Context From the Paper

**DOCUMENT CONTENT TO ANALYZE:**

=== ABSTRACT ===
Importance: Pain and mesh exposure in polypropylene mesh augmented pelvic organ prolapse repairs are linked to stiffness mismatches and mesh deformations (pore collapse and wrinkling). To overcome these limitations, we are developing novel elastomeric membranes (EM) that are macroporous and fabricated from a material that is softer than polypropylene (e.g. polycarbonate urethane) and more closely matches the stiffness of the vagina. Objective: This study assessed how pore geometry (auxetic-bowtie vs non-auxetic square or diamond) and material distribution impact the elongation, wrinkling, and porosity of EMs in response to tensile loads using finite element (FE) simulations. Study design: Nine models with varied strut dimensions were designed, maintaining constant total volume, device length, and device width. FE analysis based on a Neo-Hookean material model was used to apply 15 N uniaxial tensile loads in flat clamped and suture-like configurations. Results: For both configurations, diamond-pore membranes showed greatest elongation and porosity loss and square pore membranes showed the least. The suture-like configurations caused wrinkling that was most pronounced in bowtie-pore membranes, especially near attachment points, and least in square-pore membranes. The elongation of bowtie-pore membranes was most sensitive to material distribution, which also directly corresponded to the degree of wrinkling. Conclusion: Square pore geometry offered superior stability in response to uniaxial tension. Auxetic bowtie models showed porosity advantages, but material distribution impacted its elongation and corresponding propensity to wrinkle. These findings are critical for optimizing membrane design to minimize complications. Why this matters? (200/200 words) Current surgical meshes for pelvic organ prolapse (POP) repair remain largely adapted from hernia meshes, despite profound biomechanical and anatomical differences between the abdominal wall and the vagina. These devices are limited by their high stiffness and tendency for pore collapse and wrinkling when loaded which contributes directly to complications like chronic pain and mesh exposure. Improvements in prolapse-specific designs have not eliminated these issues, as the fundamental knitted polypropylene design remains restrictive. To address this unmet need, our work explores a vagina-centric solution-an elastomeric membrane (EM) manufactured from a material softer and more compliant than polypropylene, engineered with macropores to maintain porosity while minimizing wrinkling. Using finite element simulations, we explore the pore geometry and material distribution as key determinants of device stability. Square pore geometries consistently provided superior resistance to wrinkling and pore collapse under tensile loading, while diamond pores were highly unstable and auxetic bowtie pores, though resistant to collapse, exhibited pronounced wrinkling. These findings align with prior in vivo and ex vivo studies, underscoring the translational relevance. Importantly, this work establishes a cost-efficient computational framework for guiding next-generation device design, enabling safer, more effective prolapse repairs and providing surgeons with rational criteria for device selection.


=== TITLE ===
In silico Evaluation of an Elastomeric Membrane for Prolapse Repairs


=== DISCUSSION ===

Deformations like wrinkling and pore collapse contribute to complications observed when an polypropylene mesh is placed on tension during and following repairs of pelvic organ prolapse 15 (ie with standing, cough, sneeze etc). To overcome these behaviors, we have developed a novel softer device designed to conform to varied vaginal geometries with the goal of also minimizing deformations that have been shown to be consistent with complications. Our 3D printed porous elastomeric membrane is manufactured from PCU. In this study, computational modeling was used to inform an optimal pore geometry for the final iterations of the device. We asked how changing pore geometry (auxetic vs non-auxetic) and the material distribution within the strut width and device thickness impacted wrinkling behavior, displacement, and porosity of membranes. Pore geometries were either auxetic (bowtie) or non-auxetic (square and diamond) and models were created with narrow struts and a deep device depth, medium strut width and device depth, or wide struts and a shallow device depth. We looked at two independent boundary conditions, one in which both ends of the membranes were clamped to maintain a relatively flat configuration and a second, more clinically relevant condition in which sutures were simulated at opposing ends to mimic sewing to the sacrum and vagina. Regarding the primary outcome, bowtie models wrinkled the most followed by the diamond models. The square models, in contrast, were very stable remaining relatively flat (i.e., the square models did not wrinkle) in all conditions. Additionally, altering the strut width and device depth impacted the wrinkling behavior of the bowtie models the most with the Bowtie narrow-deep model wrinkling more than the Bowtie mid-mid and wideshallow models. Changing the strut width and device depth had minimal impact on the propensity of the diamond models to wrinkle, as unique wrinkling patterns were observed for all material distributions. Congruent with the auxetic design, the bowtie models experienced the greatest increase in porosity with loading while the greatest decrease in porosity (i.e., pore collapse) was observed for the diamond models. As anticipated, the pores of the square models remained open with very little increase in porosity in response to loading. Across all model combinations, the diamond models were the most unstable displacing the greatest amount as compared to the square models which displaced the least. Collectively, these results demonstrate that the gains in porosity of adopting an auxetic pore geometry are offset by its increasing propensity to wrinkle. On the other hand, while the porosity gains of the square pore geometry were not as significant, the pores did become larger, and that in combination with the minimal wrinkling and elongation indicated that it is the most favorable pore geometry a tensile loading application of this product. Of course, rotating a square pore by 45 degrees with respect to the direction of tension results in a diamond geometry, so it is critical that the alignment of the pores with respect to the direction of tension is considered.
Wrinkling of thin films/membranes stems from the inability of the film/membrane to withstand lateral compressive stresses when being pulled longitudinally, and that ultimately results in out-of-plane deformation (i.e., wrinkling) 29 . Thus, it was thought that the lateral expansion that results from the auxetic pore geometry would serve to flatten the device as has been shown previously 30 ; however, it seems to have served to destabilize it with respect to out-of-plane deformations in this study, which resulted in the wrinkling observed for auxetic bowtie models that was contrary to the study hypothesis. The increased propensity of the bowtie models to wrinkle may be related to the size of the unit cells (pores) with respect to the membrane dimensions and a result that is specific to a macroporous design. 31,32 Another interesting finding was that wide-shallow membranes tended to be more stable than narrow-deep membranes. Based on wrinkling theory of continuous materials, it is anticipated that the opposite would be true. However, it should be noted that this finding is likely to change if we had used a fixed displacement (i.e., all membranes loaded until they had displaced the same distance) which is less typical of in vivo surgical loading conditions. The results of this study showed that material distribution resulted in changes in elongation for auxetic and diamond membranes, with less elongation for wide-shallow devices being due to having more material distributed in a way to prevent pore deformation. In addition to the depth of the device, wrinkling theory also shows that longitudinal deformation is also a major factor in wrinkling. Therefore, while making a device shallower will increase the propensity for it to wrinkle, strategically redistributing that material to prevent elongation of the membrane can serve to limit that increased propensity.
Lastly, in an ex vivo mechanical study performed by Barone et al 2015, a square pore prolapse mesh was shown to wrinkle a lot more relative to the square models in the current study 16 .
The discrepancy in the results may be contributed to the differences in manufacturing. The polypropylene meshes assessed by Barone et al 2015 were knitted (i.e., consisting of fibers and knots) whereas the models in the current study are essentially porous membranes (with struts and no knots) and are designed to be manufactured via 3D printing or modeling. The minimal wrinkling observed in the current study suggests that manufacturing devices without knitting may minimize wrinkling and reduce the risk of complications. Future ex vivo mechanical studies comparing the wrinkling behavior of knitted vs non-knitted devices are needed to assess the impact of manufacturing technique on wrinkling behavior. Ongoing studies utilizing ex vivo mechanical testing and quantitative methods are warranted to confirm these findings.
In this study, all models were designed with the same dimensions (length and width), the same pore area, and a constant mass. Additionally, the overall volume of the models was kept constant across all models by re-distributing the material gained or lost in the width of the strut (xand y-direction) to the thickness of the device (z-direction). Maintaining consistencies in the design of the models is a major strength of this study as it allowed for the isolation of the impact of changing strut dimensions and pore geometry on the outcomes of this study (i.e., wrinkling, porosity, and displacement). Additionally, utilizing the clamped configuration (i.e., BC 1) minimized wrinkling, thus elucidating how each pore geometry (i.e., bowtie, square, and diamond) influenced porosity. Lastly, finite element analysis (FEA) was a cost-effective and efficient method to assess the impact of various design parameters on device performance without having to manufacture and perform ex vivo mechanical testing. It is important to note that this is a computational study and, while that give us great control over study variables, some of the assumptions made will impact the ability to translate the absolute values determined in this study to the real world. In particular, the assumption of hyperelastic Neo-Hookean material model to represent PCU is an approximation. Nevertheless, while absolute values may differ for an actual PCU device, the trends and overall conclusions of this study are very likely to be applicable. It is also important for the reader to note when interpreting the results of this study is that one can design a device that is mechanically superior (i.e., meets all requirement from a mechanical behavior perspective) yet does not produce a favorable host response. To date, in vivo evidence exists that demonstrates square pore meshes are more advantageous than a diamond pore mesh and while that is likely to translate to membranes based on the finding of this study, the biologic response to material distribution that is independent from the mechanical behavior it imparts is still unknown.
Overall, this study demonstrates that the square pore geometry, as compared to the diamond and bowtie, is the most stable pore geometry in response to tensile loads as characterized by negligible wrinkling and a minimal change in pore shape, porosity, and displacement irrespective of material distribution. The diamond geometry on the other hand is unstable and likely more susceptible to complications. Meanwhile, the bowtie geometry is beneficial for enhancing porosity and future in vivo studies are warranted to determine whether the unique behavior can enhance outcomes, the unanticipated degree of wrinkling is a significant tradeoff. Ultimately, the results of this study inform the design of future devices for prolapse repairs that reduces the risk of complications. the strut width (top) and device depth (bottom). Note, pictures represent only a portion of the full-length models used for FEA. B. Uniaxial tensile tests were stimulated utilizing two boundary conditions. Boundary condition 1 (left), the top and bottom edges were fully clamped to simulate a typical uniaxial tensile experimental testing set-up. Boundary condition 2 (right), four boxes were attached to the top and bottom edges (two per edge) to simulate suture attachments. Arrows represent traction force applied. condition 1. Overall, the Square membranes displaced the least while the Diamond membranes displaced the most. condition 2. Note each top view is scaled to equal width for visualization. Displacement decreases with increasing strut width for Bowtie and Diamond membranes but remains stable across Square membranes. The wrinkling pattern is distinct between pore shapes.  membrane (i.e., from top to bottom). The bowtie models wrinkle more than the diamond or    Membrane Relative Porosity Change -Boundary Condition 1: Clamped Relative Porosity Change -Boundary Condition 2: Suture Bowtie Narrow-Deep 14.3% 12.3% Bowtie Mid-Mid 11.0% 10.4% Bowtie Wide-Shallow 9.3% 9.4% Diamond Narrow-Deep -8.2% -10.8% Diamond Mid-Mid -5.1% -7.2% Diamond Wide-Shallow -1.6% -3.1% Square Narrow-Deep 1.4% 0.6% Square Mid-Mid 1.9% 1.3% Square Wide-Shallow 2.6% 2.2% Table 1. Percent change in porosity for boundary conditions 1 and 2, relative to the undeformed configuration. , from top to bottom). The bowtie models wrinkle more than the diamond or square models. For this pore shape, the middle is the most curved whereas the regions near the suture attachments remain relatively flatter. Material distribution for the bowtie pores does not have a distinct effect. By contrast, the Diamond narrowdeep model exhibits the inverse shape where the most curved regions are near the attachment points. However, the Mid and Wide models show more more uniform behavior along the length than the narrow-deep model. The square pore models remarkably resist wrinkling where the Wide-Shallow model has a marked uniform increase compared to the narrow or mid models. This supports the conclusion that square pores remain flat where auxetic pores seem to drive wrinkling. Models that have a shallower depth exhibit more consistent behavior along the length when compared to models with a deep depth in the bowtie and diamond groups.


=== INTRODUCTION ===

Pelvic organ prolapse (POP) is a common costly condition in which the pelvic organs descend from their normal anatomic position in the pelvis into the vagina, and often through the vaginal opening. Women with symptomatic POP often experience a vaginal bulge and pressure, pain, sexual dysfunction, and dysfunction with voiding and defecation [1][2][3] . Approximately 13% of U.S.
women will undergo a surgery to repair POP in their lifetime, with surgical costs exceeding $1.5 billion annually 4,5 . By 2050, it is estimated that the number of women with POP will increase 50%, posing a significant health and economic burden on society 6 .
Surgeons utilize knitted synthetic meshes to reinforce POP repairs. Originally designed for hernia repairs, synthetic meshes used in POP repairs are associated with complications, most notably pain and exposure of mesh fibers through the vaginal epithelium (mesh exposure). These complications occur in roughly 10% of cases, and this rate increases with time [7][8][9][10] . Research suggests that stiffness mismatches between the mesh and the vagina and mesh deformation (pore collapse and wrinkling) with tensioning contribute to mesh complications [11][12][13][14][15] . Specifically, studies implanting synthetic meshes with varying stiffnesses onto the vagina of nonhuman primates have demonstrated that meshes with high structural stiffness are associated with a maladaptive remodeling response that results in vaginal degeneration indicative of stress shielding [11][12][13][14] . Ex vivo mechanical testing and computational modeling studies have shown that the pores of most synthetic meshes tend to collapse and the meshes wrinkle in response to tension [16][17][18][19] . Indeed, implanting meshes with collapsed pores and wrinkles onto the nonhuman primate vagina effectively reproduced complications with mesh exposures, loss of vaginal rugae, and vaginal thinning consistent with what is observed clinically 15 . Collectively these results highlight an emergent need for an alternative device for POP repairs that is made from a soft material, maintains porosity (i.e., the pores do not collapse), and minimizes wrinkling when tensioned.
To address this need, we are developing novel elastomeric membranes. Previously, we highlighted that changing the pores to an auxetic geometry obviates pore collapse 20 . Auxetic pore geometries have a negative Poisson's ratio; that is, unlike typical everyday materials, they expand in the transverse direction when stretched/tensioned longitudinally. We refer to our devices as membranes, as opposed to meshes, since they are a continuous piece of material with macroscopic pores. Meshes, on the other hand, are composed of multiple thin fibers (often made from polypropylene) that are knitted. Knitting is necessary because a continuous piece of polypropylene, even with macroscopic pores punched out, would result in a device with very little compliance, similar to that of a Tupperware TM container. Since our membrane is manufactured from polycarbonate urethane (PCU), a polymer that is substantially softer than polypropylene and can be tuned to match the material properties of the vagina by varying its hard and soft components, it allows for a macroporous membrane design. Additionally, given our device is not knitted with fibers, we refer to the regions of material bounding the pores as struts (as opposed to fibers). We have shown that membranes developed from PCU have a similar failure load and fatigue resistance to a commercially available polypropylene mesh (Restorelle, Coloplast, Coloplast, Minneapolis, MN, USA) 21 . PCU is an elastomer and as such, is less susceptible to permanent deformation with loading. At the same time, its toughness exceeds that of most elastomers.
Finite element analysis is a computational modeling framework that allows for controlled and cost-effective simulations of POP repair devices exposed to loading conditions that mimic surgical repair prior to or as a supplement to implantation. In the current study, finite element analysis was used to inform how design parameters, specifically pore shape (auxetic vs non-auxetic) and material distribution (dimensions of the struts), impact the propensity of the PCU membranes to wrinkle (nonplanar deformation), since a final design associated with the least amount of wrinkling is a highly desirable endpoint. As a secondary outcome, the impact of design parameters on elongation (change in length with tension) and porosity were also assessed. Based on previous studies, we examined the "bowtie" shape as our auxetic pore geometry (selected based on a previous computational study) 20 and used diamond and square shapes as non-auxetic geometries since the pores of current polypropylene meshes are typically square or diamond shaped. Two different loading configurations were examined in our finite element simulations. In the first, typical of uniaxial tensile tests that are often used to evaluate the mechanical behavior of POP devices, the devices (15 x 5 cm) were tensioned by simulating clamps affixed to both of its shorter ends such that tension could be applied along the long axis of the device. For the second loading configuration, the clamps were removed and tension was applied to two small areas on each of the opposing ends of the device to simulate sutures, a scenario that more closely mimics tension applied during surgery. The boundary conditions of the second loading configuration are less constrained and likely to reveal mechanical instabilities that favor wrinkling. We hypothesized that the auxetic behavior would have the lowest amount of wrinkling and highest porosity and that membranes that were thinner in depth would have an increased propensity to wrinkle.

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents original computational research, including a finite element analysis of novel elastomeric membranes, with structured sections (Abstract, Introduction, Methods/Discussion, Results). It reports original findings rather than a systematic review.  
**CONTEXT:** “Objective: This study assessed how pore geometry … and material distribution impact the elongation, wrinkling, and porosity of EMs in response to tensile loads using finite element (FE) simulations.” (Abstract) and “In this study, finite element analysis was used to inform how design parameters, specifically pore shape … impact the propensity of the PCU membranes to wrinkle.” (Introduction) These passages indicate original research rather than a systematic review.

**RESULT:** PASS  
**EXPLANATION:** The manuscript reports original experimental work using finite element analysis to evaluate pore geometry and material distribution in elastomeric membranes for pelvic organ prolapse repair. It contains the standard structure of a research paper—Abstract, Introduction, Methods (finite element modeling), Results (elongation, wrinkling, porosity), and Discussion—indicating it is a primary research study rather than a systematic review.  
**CONTEXT:**  
- Abstract: “Objective: This study assessed how pore geometry… using finite element (FE) simulations.”  
- Introduction: “Finite element analysis is a computational modeling framework… In the current study, finite element analysis was used to inform how design parameters… impact the propensity of the PCU membranes to wrinkle.”  
- Discussion: “In this study, computational modeling was used to inform an optimal pore geometry… Finite element analysis (FEA) was a cost-effective and efficient method to assess the impact of various design parameters.”

=== DISCUSSION ===

Deformations like wrinkling and pore collapse contribute to complications observed when an polypropylene mesh is placed on tension during and following repairs of pelvic organ prolapse 15 (ie with standing, cough, sneeze etc). To overcome these behaviors, we have developed a novel softer device designed to conform to varied vaginal geometries with the goal of also minimizing deformations that have been shown to be consistent with complications. Our 3D printed porous elastomeric membrane is manufactured from PCU. In this study, computational modeling was used to inform an optimal pore geometry for the final iterations of the device. We asked how changing pore geometry (auxetic vs non-auxetic) and the material distribution within the strut width and device thickness impacted wrinkling behavior, displacement, and porosity of membranes. Pore geometries were either auxetic (bowtie) or non-auxetic (square and diamond) and models were created with narrow struts and a deep device depth, medium strut width and device depth, or wide struts and a shallow device depth. We looked at two independent boundary conditions, one in which both ends of the membranes were clamped to maintain a relatively flat configuration and a second, more clinically relevant condition in which sutures were simulated at opposing ends to mimic sewing to the sacrum and vagina. Regarding the primary outcome, bowtie models wrinkled the most followed by the diamond models. The square models, in contrast, were very stable remaining relatively flat (i.e., the square models did not wrinkle) in all conditions. Additionally, altering the strut width and device depth impacted the wrinkling behavior of the bowtie models the most with the Bowtie narrow-deep model wrinkling more than the Bowtie mid-mid and wideshallow models. Changing the strut width and device depth had minimal impact on the propensity of the diamond models to wrinkle, as unique wrinkling patterns were observed for all material distributions. Congruent with the auxetic design, the bowtie models experienced the greatest increase in porosity with loading while the greatest decrease in porosity (i.e., pore collapse) was observed for the diamond models. As anticipated, the pores of the square models remained open with very little increase in porosity in response to loading. Across all model combinations, the diamond models were the most unstable displacing the greatest amount as compared to the square models which displaced the least. Collectively, these results demonstrate that the gains in porosity of adopting an auxetic pore geometry are offset by its increasing propensity to wrinkle. On the other hand, while the porosity gains of the square pore geometry were not as significant, the pores did become larger, and that in combination with the minimal wrinkling and elongation indicated that it is the most favorable pore geometry a tensile loading application of this product. Of course, rotating a square pore by 45 degrees with respect to the direction of tension results in a diamond geometry, so it is critical that the alignment of the pores with respect to the direction of tension is considered.
Wrinkling of thin films/membranes stems from the inability of the film/membrane to withstand lateral compressive stresses when being pulled longitudinally, and that ultimately results in out-of-plane deformation (i.e., wrinkling) 29 . Thus, it was thought that the lateral expansion that results from the auxetic pore geometry would serve to flatten the device as has been shown previously 30 ; however, it seems to have served to destabilize it with respect to out-of-plane deformations in this study, which resulted in the wrinkling observed for auxetic bowtie models that was contrary to the study hypothesis. The increased propensity of the bowtie models to wrinkle may be related to the size of the unit cells (pores) with respect to the membrane dimensions and a result that is specific to a macroporous design. 31,32 Another interesting finding was that wide-shallow membranes tended to be more stable than narrow-deep membranes. Based on wrinkling theory of continuous materials, it is anticipated that the opposite would be true. However, it should be noted that this finding is likely to change if we had used a fixed displacement (i.e., all membranes loaded until they had displaced the same distance) which is less typical of in vivo surgical loading conditions. The results of this study showed that material distribution resulted in changes in elongation for auxetic and diamond membranes, with less elongation for wide-shallow devices being due to having more material distributed in a way to prevent pore deformation. In addition to the depth of the device, wrinkling theory also shows that longitudinal deformation is also a major factor in wrinkling. Therefore, while making a device shallower will increase the propensity for it to wrinkle, strategically redistributing that material to prevent elongation of the membrane can serve to limit that increased propensity.
Lastly, in an ex vivo mechanical study performed by Barone et al 2015, a square pore prolapse mesh was shown to wrinkle a lot more relative to the square models in the current study 16 .
The discrepancy in the results may be contributed to the differences in manufacturing. The polypropylene meshes assessed by Barone et al 2015 were knitted (i.e., consisting of fibers and knots) whereas the models in the current study are essentially porous membranes (with struts and no knots) and are designed to be manufactured via 3D printing or modeling. The minimal wrinkling observed in the current study suggests that manufacturing devices without knitting may minimize wrinkling and reduce the risk of complications. Future ex vivo mechanical studies comparing the wrinkling behavior of knitted vs non-knitted devices are needed to assess the impact of manufacturing technique on wrinkling behavior. Ongoing studies utilizing ex vivo mechanical testing and quantitative methods are warranted to confirm these findings.
In this study, all models were designed with the same dimensions (length and width), the same pore area, and a constant mass. Additionally, the overall volume of the models was kept constant across all models by re-distributing the material gained or lost in the width of the strut (xand y-direction) to the thickness of the device (z-direction). Maintaining consistencies in the design of the models is a major strength of this study as it allowed for the isolation of the impact of changing strut dimensions and pore geometry on the outcomes of this study (i.e., wrinkling, porosity, and displacement). Additionally, utilizing the clamped configuration (i.e., BC 1) minimized wrinkling, thus elucidating how each pore geometry (i.e., bowtie, square, and diamond) influenced porosity. Lastly, finite element analysis (FEA) was a cost-effective and efficient method to assess the impact of various design parameters on device performance without having to manufacture and perform ex vivo mechanical testing. It is important to note that this is a computational study and, while that give us great control over study variables, some of the assumptions made will impact the ability to translate the absolute values determined in this study to the real world. In particular, the assumption of hyperelastic Neo-Hookean material model to represent PCU is an approximation. Nevertheless, while absolute values may differ for an actual PCU device, the trends and overall conclusions of this study are very likely to be applicable. It is also important for the reader to note when interpreting the results of this study is that one can design a device that is mechanically superior (i.e., meets all requirement from a mechanical behavior perspective) yet does not produce a favorable host response. To date, in vivo evidence exists that demonstrates square pore meshes are more advantageous than a diamond pore mesh and while that is likely to translate to membranes based on the finding of this study, the biologic response to material distribution that is independent from the mechanical behavior it imparts is still unknown.
Overall, this study demonstrates that the square pore geometry, as compared to the diamond and bowtie, is the most stable pore geometry in response to tensile loads as characterized by negligible wrinkling and a minimal change in pore shape, porosity, and displacement irrespective of material distribution. The diamond geometry on the other hand is unstable and likely more susceptible to complications. Meanwhile, the bowtie geometry is beneficial for enhancing porosity and future in vivo studies are warranted to determine whether the unique behavior can enhance outcomes, the unanticipated degree of wrinkling is a significant tradeoff. Ultimately, the results of this study inform the design of future devices for prolapse repairs that reduces the risk of complications. the strut width (top) and device depth (bottom). Note, pictures represent only a portion of the full-length models used for FEA. B. Uniaxial tensile tests were stimulated utilizing two boundary conditions. Boundary condition 1 (left), the top and bottom edges were fully clamped to simulate a typical uniaxial tensile experimental testing set-up. Boundary condition 2 (right), four boxes were attached to the top and bottom edges (two per edge) to simulate suture attachments. Arrows represent traction force applied. condition 1. Overall, the Square membranes displaced the least while the Diamond membranes displaced the most. condition 2. Note each top view is scaled to equal width for visualization. Displacement decreases with increasing strut width for Bowtie and Diamond membranes but remains stable across Square membranes. The wrinkling pattern is distinct between pore shapes.  membrane (i.e., from top to bottom). The bowtie models wrinkle more than the diamond or    Membrane Relative Porosity Change -Boundary Condition 1: Clamped Relative Porosity Change -Boundary Condition 2: Suture Bowtie Narrow-Deep 14.3% 12.3% Bowtie Mid-Mid 11.0% 10.4% Bowtie Wide-Shallow 9.3% 9.4% Diamond Narrow-Deep -8.2% -10.8% Diamond Mid-Mid -5.1% -7.2% Diamond Wide-Shallow -1.6% -3.1% Square Narrow-Deep 1.4% 0.6% Square Mid-Mid 1.9% 1.3% Square Wide-Shallow 2.6% 2.2% Table 1. Percent change in porosity for boundary conditions 1 and 2, relative to the undeformed configuration. , from top to bottom). The bowtie models wrinkle more than the diamond or square models. For this pore shape, the middle is the most curved whereas the regions near the suture attachments remain relatively flatter. Material distribution for the bowtie pores does not have a distinct effect. By contrast, the Diamond narrowdeep model exhibits the inverse shape where the most curved regions are near the attachment points. However, the Mid and Wide models show more more uniform behavior along the length than the narrow-deep model. The square pore models remarkably resist wrinkling where the Wide-Shallow model has a marked uniform increase compared to the narrow or mid models. This supports the conclusion that square pores remain flat where auxetic pores seem to drive wrinkling. Models that have a shallower depth exhibit more consistent behavior along the length when compared to models with a deep depth in the bowtie and diamond groups.


=== INTRODUCTION ===

Pelvic organ prolapse (POP) is a common costly condition in which the pelvic organs descend from their normal anatomic position in the pelvis into the vagina, and often through the vaginal opening. Women with symptomatic POP often experience a vaginal bulge and pressure, pain, sexual dysfunction, and dysfunction with voiding and defecation [1][2][3] . Approximately 13% of U.S.
women will undergo a surgery to repair POP in their lifetime, with surgical costs exceeding $1.5 billion annually 4,5 . By 2050, it is estimated that the number of women with POP will increase 50%, posing a significant health and economic burden on society 6 .
Surgeons utilize knitted synthetic meshes to reinforce POP repairs. Originally designed for hernia repairs, synthetic meshes used in POP repairs are associated with complications, most notably pain and exposure of mesh fibers through the vaginal epithelium (mesh exposure). These complications occur in roughly 10% of cases, and this rate increases with time [7][8][9][10] . Research suggests that stiffness mismatches between the mesh and the vagina and mesh deformation (pore collapse and wrinkling) with tensioning contribute to mesh complications [11][12][13][14][15] . Specifically, studies implanting synthetic meshes with varying stiffnesses onto the vagina of nonhuman primates have demonstrated that meshes with high structural stiffness are associated with a maladaptive remodeling response that results in vaginal degeneration indicative of stress shielding [11][12][13][14] . Ex vivo mechanical testing and computational modeling studies have shown that the pores of most synthetic meshes tend to collapse and the meshes wrinkle in response to tension [16][17][18][19] . Indeed, implanting meshes with collapsed pores and wrinkles onto the nonhuman primate vagina effectively reproduced complications with mesh exposures, loss of vaginal rugae, and vaginal thinning consistent with what is observed clinically 15 . Collectively these results highlight an emergent need for an alternative device for POP repairs that is made from a soft material, maintains porosity (i.e., the pores do not collapse), and minimizes wrinkling when tensioned.
To address this need, we are developing novel elastomeric membranes. Previously, we highlighted that changing the pores to an auxetic geometry obviates pore collapse 20 . Auxetic pore geometries have a negative Poisson's ratio; that is, unlike typical everyday materials, they expand in the transverse direction when stretched/tensioned longitudinally. We refer to our devices as membranes, as opposed to meshes, since they are a continuous piece of material with macroscopic pores. Meshes, on the other hand, are composed of multiple thin fibers (often made from polypropylene) that are knitted. Knitting is necessary because a continuous piece of polypropylene, even with macroscopic pores punched out, would result in a device with very little compliance, similar to that of a Tupperware TM container. Since our membrane is manufactured from polycarbonate urethane (PCU), a polymer that is substantially softer than polypropylene and can be tuned to match the material properties of the vagina by varying its hard and soft components, it allows for a macroporous membrane design. Additionally, given our device is not knitted with fibers, we refer to the regions of material bounding the pores as struts (as opposed to fibers). We have shown that membranes developed from PCU have a similar failure load and fatigue resistance to a commercially available polypropylene mesh (Restorelle, Coloplast, Coloplast, Minneapolis, MN, USA) 21 . PCU is an elastomer and as such, is less susceptible to permanent deformation with loading. At the same time, its toughness exceeds that of most elastomers.
Finite element analysis is a computational modeling framework that allows for controlled and cost-effective simulations of POP repair devices exposed to loading conditions that mimic surgical repair prior to or as a supplement to implantation. In the current study, finite element analysis was used to inform how design parameters, specifically pore shape (auxetic vs non-auxetic) and material distribution (dimensions of the struts), impact the propensity of the PCU membranes to wrinkle (nonplanar deformation), since a final design associated with the least amount of wrinkling is a highly desirable endpoint. As a secondary outcome, the impact of design parameters on elongation (change in length with tension) and porosity were also assessed. Based on previous studies, we examined the "bowtie" shape as our auxetic pore geometry (selected based on a previous computational study) 20 and used diamond and square shapes as non-auxetic geometries since the pores of current polypropylene meshes are typically square or diamond shaped. Two different loading configurations were examined in our finite element simulations. In the first, typical of uniaxial tensile tests that are often used to evaluate the mechanical behavior of POP devices, the devices (15 x 5 cm) were tensioned by simulating clamps affixed to both of its shorter ends such that tension could be applied along the long axis of the device. For the second loading configuration, the clamps were removed and tension was applied to two small areas on each of the opposing ends of the device to simulate sutures, a scenario that more closely mimics tension applied during surgery. The boundary conditions of the second loading configuration are less constrained and likely to reveal mechanical instabilities that favor wrinkling. We hypothesized that the auxetic behavior would have the lowest amount of wrinkling and highest porosity and that membranes that were thinner in depth would have an increased propensity to wrinkle.


=== ABSTRACT ===
Importance: Pain and mesh exposure in polypropylene mesh augmented pelvic organ prolapse repairs are linked to stiffness mismatches and mesh deformations (pore collapse and wrinkling). To overcome these limitations, we are developing novel elastomeric membranes (EM) that are macroporous and fabricated from a material that is softer than polypropylene (e.g. polycarbonate urethane) and more closely matches the stiffness of the vagina. Objective: This study assessed how pore geometry (auxetic-bowtie vs non-auxetic square or diamond) and material distribution impact the elongation, wrinkling, and porosity of EMs in response to tensile loads using finite element (FE) simulations. Study design: Nine models with varied strut dimensions were designed, maintaining constant total volume, device length, and device width. FE analysis based on a Neo-Hookean material model was used to apply 15 N uniaxial tensile loads in flat clamped and suture-like configurations. Results: For both configurations, diamond-pore membranes showed greatest elongation and porosity loss and square pore membranes showed the least. The suture-like configurations caused wrinkling that was most pronounced in bowtie-pore membranes, especially near attachment points, and least in square-pore membranes. The elongation of bowtie-pore membranes was most sensitive to material distribution, which also directly corresponded to the degree of wrinkling. Conclusion: Square pore geometry offered superior stability in response to uniaxial tension. Auxetic bowtie models showed porosity advantages, but material distribution impacted its elongation and corresponding propensity to wrinkle. These findings are critical for optimizing membrane design to minimize complications. Why this matters? (200/200 words) Current surgical meshes for pelvic organ prolapse (POP) repair remain largely adapted from hernia meshes, despite profound biomechanical and anatomical differences between the abdominal wall and the vagina. These devices are limited by their high stiffness and tendency for pore collapse and wrinkling when loaded which contributes directly to complications like chronic pain and mesh exposure. Improvements in prolapse-specific designs have not eliminated these issues, as the fundamental knitted polypropylene design remains restrictive. To address this unmet need, our work explores a vagina-centric solution-an elastomeric membrane (EM) manufactured from a material softer and more compliant than polypropylene, engineered with macropores to maintain porosity while minimizing wrinkling. Using finite element simulations, we explore the pore geometry and material distribution as key determinants of device stability. Square pore geometries consistently provided superior resistance to wrinkling and pore collapse under tensile loading, while diamond pores were highly unstable and auxetic bowtie pores, though resistant to collapse, exhibited pronounced wrinkling. These findings align with prior in vivo and ex vivo studies, underscoring the translational relevance. Importantly, this work establishes a cost-efficient computational framework for guiding next-generation device design, enabling safer, more effective prolapse repairs and providing surgeons with rational criteria for device selection.


=== TITLE ===
In silico Evaluation of an Elastomeric Membrane for Prolapse Repairs

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

Objective  
Confirm whether the manuscript includes new data or qualifies as a clinical research protocol, based on content required by medRxiv.

Instructions 
1. Analyze the manuscript for the presence of either:
	- New data from experiments, observations, clinical trials, or simulations, even if based on previously published sources (e.g., meta-analyses with new synthesis).
	- For a medRxiv submission, check whether the submission is a clinical research protocol, defined as a structured plan for a clinical study.
2. Treat meta-analyses and data syntheses from prior studies as valid forms of ""data"" for this check, if they involve original re-analysis or synthesis.
3. To confirm presence of new data:
	- Review tables, figures, or data presentations that are not directly reused from prior published work.
	- Look for explicit mentions of new findings or original analysis.
4. To confirm a clinical research protocol:
	- Check for structured information on study objectives, design, methods, eligibility criteria, or trial phases.
	- Trial registration numbers or ethics review statements are useful indicators.
5. Mark the manuscript as FAIL if neither new data nor clinical protocol indicators are found.
	- If the content is ambiguous or borderline, mark as FAIL with a clear explanation so that a human screener can review the context.

Output
1. Provide a PASS or FAIL result
	- Has New Data Or Is A Clinical Research Protocol: PASS
	- Does Not Have New Data Or A Clinical Research Protocol: FAIL
2. Provide a Detailed Explanation to evidence the PASS or FAIL result
3. Provide the context From the Paper to evidence the PASS or FAIL result

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

**SPECIFIC CHECK:**
Objective  
Confirm whether the manuscript includes new data or qualifies as a clinical research protocol, based on content required by medRxiv.

Instructions 
1. Analyze the manuscript for the presence of either:
	- New data from experiments, observations, clinical trials, or simulations, even if based on previously published sources (e.g., meta-analyses with new synthesis).
	- For a medRxiv submission, check whether the submission is a clinical research protocol, defined as a structured plan for a clinical study.
2. Treat meta-analyses and data syntheses from prior studies as valid forms of ""data"" for this check, if they involve original re-analysis or synthesis.
3. To confirm presence of new data:
	- Review tables, figures, or data presentations that are not directly reused from prior published work.
	- Look for explicit mentions of new findings or original analysis.
4. To confirm a clinical research protocol:
	- Check for structured information on study objectives, design, methods, eligibility criteria, or trial phases.
	- Trial registration numbers or ethics review statements are useful indicators.
5. Mark the manuscript as FAIL if neither new data nor clinical protocol indicators are found.
	- If the content is ambiguous or borderline, mark as FAIL with a clear explanation so that a human screener can review the context.

Output
1. Provide a PASS or FAIL result
	- Has New Data Or Is A Clinical Research Protocol: PASS
	- Does Not Have New Data Or A Clinical Research Protocol: FAIL
2. Provide a Detailed Explanation to evidence the PASS or FAIL result
3. Provide the context From the Paper to evidence the PASS or FAIL result



**DOCUMENT CONTENT TO ANALYZE:**

=== DISCUSSION ===

Deformations like wrinkling and pore collapse contribute to complications observed when an polypropylene mesh is placed on tension during and following repairs of pelvic organ prolapse 15 (ie with standing, cough, sneeze etc). To overcome these behaviors, we have developed a novel softer device designed to conform to varied vaginal geometries with the goal of also minimizing deformations that have been shown to be consistent with complications. Our 3D printed porous elastomeric membrane is manufactured from PCU. In this study, computational modeling was used to inform an optimal pore geometry for the final iterations of the device. We asked how changing pore geometry (auxetic vs non-auxetic) and the material distribution within the strut width and device thickness impacted wrinkling behavior, displacement, and porosity of membranes. Pore geometries were either auxetic (bowtie) or non-auxetic (square and diamond) and models were created with narrow struts and a deep device depth, medium strut width and device depth, or wide struts and a shallow device depth. We looked at two independent boundary conditions, one in which both ends of the membranes were clamped to maintain a relatively flat configuration and a second, more clinically relevant condition in which sutures were simulated at opposing ends to mimic sewing to the sacrum and vagina. Regarding the primary outcome, bowtie models wrinkled the most followed by the diamond models. The square models, in contrast, were very stable remaining relatively flat (i.e., the square models did not wrinkle) in all conditions. Additionally, altering the strut width and device depth impacted the wrinkling behavior of the bowtie models the most with the Bowtie narrow-deep model wrinkling more than the Bowtie mid-mid and wideshallow models. Changing the strut width and device depth had minimal impact on the propensity of the diamond models to wrinkle, as unique wrinkling patterns were observed for all material distributions. Congruent with the auxetic design, the bowtie models experienced the greatest increase in porosity with loading while the greatest decrease in porosity (i.e., pore collapse) was observed for the diamond models. As anticipated, the pores of the square models remained open with very little increase in porosity in response to loading. Across all model combinations, the diamond models were the most unstable displacing the greatest amount as compared to the square models which displaced the least. Collectively, these results demonstrate that the gains in porosity of adopting an auxetic pore geometry are offset by its increasing propensity to wrinkle. On the other hand, while the porosity gains of the square pore geometry were not as significant, the pores did become larger, and that in combination with the minimal wrinkling and elongation indicated that it is the most favorable pore geometry a tensile loading application of this product. Of course, rotating a square pore by 45 degrees with respect to the direction of tension results in a diamond geometry, so it is critical that the alignment of the pores with respect to the direction of tension is considered.
Wrinkling of thin films/membranes stems from the inability of the film/membrane to withstand lateral compressive stresses when being pulled longitudinally, and that ultimately results in out-of-plane deformation (i.e., wrinkling) 29 . Thus, it was thought that the lateral expansion that results from the auxetic pore geometry would serve to flatten the device as has been shown previously 30 ; however, it seems to have served to destabilize it with respect to out-of-plane deformations in this study, which resulted in the wrinkling observed for auxetic bowtie models that was contrary to the study hypothesis. The increased propensity of the bowtie models to wrinkle may be related to the size of the unit cells (pores) with respect to the membrane dimensions and a result that is specific to a macroporous design. 31,32 Another interesting finding was that wide-shallow membranes tended to be more stable than narrow-deep membranes. Based on wrinkling theory of continuous materials, it is anticipated that the opposite would be true. However, it should be noted that this finding is likely to change if we had used a fixed displacement (i.e., all membranes loaded until they had displaced the same distance) which is less typical of in vivo surgical loading conditions. The results of this study showed that material distribution resulted in changes in elongation for auxetic and diamond membranes, with less elongation for wide-shallow devices being due to having more material distributed in a way to prevent pore deformation. In addition to the depth of the device, wrinkling theory also shows that longitudinal deformation is also a major factor in wrinkling. Therefore, while making a device shallower will increase the propensity for it to wrinkle, strategically redistributing that material to prevent elongation of the membrane can serve to limit that increased propensity.
Lastly, in an ex vivo mechanical study performed by Barone et al 2015, a square pore prolapse mesh was shown to wrinkle a lot more relative to the square models in the current study 16 .
The discrepancy in the results may be contributed to the differences in manufacturing. The polypropylene meshes assessed by Barone et al 2015 were knitted (i.e., consisting of fibers and knots) whereas the models in the current study are essentially porous membranes (with struts and no knots) and are designed to be manufactured via 3D printing or modeling. The minimal wrinkling observed in the current study suggests that manufacturing devices without knitting may minimize wrinkling and reduce the risk of complications. Future ex vivo mechanical studies comparing the wrinkling behavior of knitted vs non-knitted devices are needed to assess the impact of manufacturing technique on wrinkling behavior. Ongoing studies utilizing ex vivo mechanical testing and quantitative methods are warranted to confirm these findings.
In this study, all models were designed with the same dimensions (length and width), the same pore area, and a constant mass. Additionally, the overall volume of the models was kept constant across all models by re-distributing the material gained or lost in the width of the strut (xand y-direction) to the thickness of the device (z-direction). Maintaining consistencies in the design of the models is a major strength of this study as it allowed for the isolation of the impact of changing strut dimensions and pore geometry on the outcomes of this study (i.e., wrinkling, porosity, and displacement). Additionally, utilizing the clamped configuration (i.e., BC 1) minimized wrinkling, thus elucidating how each pore geometry (i.e., bowtie, square, and diamond) influenced porosity. Lastly, finite element analysis (FEA) was a cost-effective and efficient method to assess the impact of various design parameters on device performance without having to manufacture and perform ex vivo mechanical testing. It is important to note that this is a computational study and, while that give us great control over study variables, some of the assumptions made will impact the ability to translate the absolute values determined in this study to the real world. In particular, the assumption of hyperelastic Neo-Hookean material model to represent PCU is an approximation. Nevertheless, while absolute values may differ for an actual PCU device, the trends and overall conclusions of this study are very likely to be applicable. It is also important for the reader to note when interpreting the results of this study is that one can design a device that is mechanically superior (i.e., meets all requirement from a mechanical behavior perspective) yet does not produce a favorable host response. To date, in vivo evidence exists that demonstrates square pore meshes are more advantageous than a diamond pore mesh and while that is likely to translate to membranes based on the finding of this study, the biologic response to material distribution that is independent from the mechanical behavior it imparts is still unknown.
Overall, this study demonstrates that the square pore geometry, as compared to the diamond and bowtie, is the most stable pore geometry in response to tensile loads as characterized by negligible wrinkling and a minimal change in pore shape, porosity, and displacement irrespective of material distribution. The diamond geometry on the other hand is unstable and likely more susceptible to complications. Meanwhile, the bowtie geometry is beneficial for enhancing porosity and future in vivo studies are warranted to determine whether the unique behavior can enhance outcomes, the unanticipated degree of wrinkling is a significant tradeoff. Ultimately, the results of this study inform the design of future devices for prolapse repairs that reduces the risk of complications. the strut width (top) and device depth (bottom). Note, pictures represent only a portion of the full-length models used for FEA. B. Uniaxial tensile tests were stimulated utilizing two boundary conditions. Boundary condition 1 (left), the top and bottom edges were fully clamped to simulate a typical uniaxial tensile experimental testing set-up. Boundary condition 2 (right), four boxes were attached to the top and bottom edges (two per edge) to simulate suture attachments. Arrows represent traction force applied. condition 1. Overall, the Square membranes displaced the least while the Diamond membranes displaced the most. condition 2. Note each top view is scaled to equal width for visualization. Displacement decreases with increasing strut width for Bowtie and Diamond membranes but remains stable across Square membranes. The wrinkling pattern is distinct between pore shapes.  membrane (i.e., from top to bottom). The bowtie models wrinkle more than the diamond or    Membrane Relative Porosity Change -Boundary Condition 1: Clamped Relative Porosity Change -Boundary Condition 2: Suture Bowtie Narrow-Deep 14.3% 12.3% Bowtie Mid-Mid 11.0% 10.4% Bowtie Wide-Shallow 9.3% 9.4% Diamond Narrow-Deep -8.2% -10.8% Diamond Mid-Mid -5.1% -7.2% Diamond Wide-Shallow -1.6% -3.1% Square Narrow-Deep 1.4% 0.6% Square Mid-Mid 1.9% 1.3% Square Wide-Shallow 2.6% 2.2% Table 1. Percent change in porosity for boundary conditions 1 and 2, relative to the undeformed configuration. , from top to bottom). The bowtie models wrinkle more than the diamond or square models. For this pore shape, the middle is the most curved whereas the regions near the suture attachments remain relatively flatter. Material distribution for the bowtie pores does not have a distinct effect. By contrast, the Diamond narrowdeep model exhibits the inverse shape where the most curved regions are near the attachment points. However, the Mid and Wide models show more more uniform behavior along the length than the narrow-deep model. The square pore models remarkably resist wrinkling where the Wide-Shallow model has a marked uniform increase compared to the narrow or mid models. This supports the conclusion that square pores remain flat where auxetic pores seem to drive wrinkling. Models that have a shallower depth exhibit more consistent behavior along the length when compared to models with a deep depth in the bowtie and diamond groups.


=== INTRODUCTION ===

Pelvic organ prolapse (POP) is a common costly condition in which the pelvic organs descend from their normal anatomic position in the pelvis into the vagina, and often through the vaginal opening. Women with symptomatic POP often experience a vaginal bulge and pressure, pain, sexual dysfunction, and dysfunction with voiding and defecation [1][2][3] . Approximately 13% of U.S.
women will undergo a surgery to repair POP in their lifetime, with surgical costs exceeding $1.5 billion annually 4,5 . By 2050, it is estimated that the number of women with POP will increase 50%, posing a significant health and economic burden on society 6 .
Surgeons utilize knitted synthetic meshes to reinforce POP repairs. Originally designed for hernia repairs, synthetic meshes used in POP repairs are associated with complications, most notably pain and exposure of mesh fibers through the vaginal epithelium (mesh exposure). These complications occur in roughly 10% of cases, and this rate increases with time [7][8][9][10] . Research suggests that stiffness mismatches between the mesh and the vagina and mesh deformation (pore collapse and wrinkling) with tensioning contribute to mesh complications [11][12][13][14][15] . Specifically, studies implanting synthetic meshes with varying stiffnesses onto the vagina of nonhuman primates have demonstrated that meshes with high structural stiffness are associated with a maladaptive remodeling response that results in vaginal degeneration indicative of stress shielding [11][12][13][14] . Ex vivo mechanical testing and computational modeling studies have shown that the pores of most synthetic meshes tend to collapse and the meshes wrinkle in response to tension [16][17][18][19] . Indeed, implanting meshes with collapsed pores and wrinkles onto the nonhuman primate vagina effectively reproduced complications with mesh exposures, loss of vaginal rugae, and vaginal thinning consistent with what is observed clinically 15 . Collectively these results highlight an emergent need for an alternative device for POP repairs that is made from a soft material, maintains porosity (i.e., the pores do not collapse), and minimizes wrinkling when tensioned.
To address this need, we are developing novel elastomeric membranes. Previously, we highlighted that changing the pores to an auxetic geometry obviates pore collapse 20 . Auxetic pore geometries have a negative Poisson's ratio; that is, unlike typical everyday materials, they expand in the transverse direction when stretched/tensioned longitudinally. We refer to our devices as membranes, as opposed to meshes, since they are a continuous piece of material with macroscopic pores. Meshes, on the other hand, are composed of multiple thin fibers (often made from polypropylene) that are knitted. Knitting is necessary because a continuous piece of polypropylene, even with macroscopic pores punched out, would result in a device with very little compliance, similar to that of a Tupperware TM container. Since our membrane is manufactured from polycarbonate urethane (PCU), a polymer that is substantially softer than polypropylene and can be tuned to match the material properties of the vagina by varying its hard and soft components, it allows for a macroporous membrane design. Additionally, given our device is not knitted with fibers, we refer to the regions of material bounding the pores as struts (as opposed to fibers). We have shown that membranes developed from PCU have a similar failure load and fatigue resistance to a commercially available polypropylene mesh (Restorelle, Coloplast, Coloplast, Minneapolis, MN, USA) 21 . PCU is an elastomer and as such, is less susceptible to permanent deformation with loading. At the same time, its toughness exceeds that of most elastomers.
Finite element analysis is a computational modeling framework that allows for controlled and cost-effective simulations of POP repair devices exposed to loading conditions that mimic surgical repair prior to or as a supplement to implantation. In the current study, finite element analysis was used to inform how design parameters, specifically pore shape (auxetic vs non-auxetic) and material distribution (dimensions of the struts), impact the propensity of the PCU membranes to wrinkle (nonplanar deformation), since a final design associated with the least amount of wrinkling is a highly desirable endpoint. As a secondary outcome, the impact of design parameters on elongation (change in length with tension) and porosity were also assessed. Based on previous studies, we examined the "bowtie" shape as our auxetic pore geometry (selected based on a previous computational study) 20 and used diamond and square shapes as non-auxetic geometries since the pores of current polypropylene meshes are typically square or diamond shaped. Two different loading configurations were examined in our finite element simulations. In the first, typical of uniaxial tensile tests that are often used to evaluate the mechanical behavior of POP devices, the devices (15 x 5 cm) were tensioned by simulating clamps affixed to both of its shorter ends such that tension could be applied along the long axis of the device. For the second loading configuration, the clamps were removed and tension was applied to two small areas on each of the opposing ends of the device to simulate sutures, a scenario that more closely mimics tension applied during surgery. The boundary conditions of the second loading configuration are less constrained and likely to reveal mechanical instabilities that favor wrinkling. We hypothesized that the auxetic behavior would have the lowest amount of wrinkling and highest porosity and that membranes that were thinner in depth would have an increased propensity to wrinkle.


=== ABSTRACT ===
Importance: Pain and mesh exposure in polypropylene mesh augmented pelvic organ prolapse repairs are linked to stiffness mismatches and mesh deformations (pore collapse and wrinkling). To overcome these limitations, we are developing novel elastomeric membranes (EM) that are macroporous and fabricated from a material that is softer than polypropylene (e.g. polycarbonate urethane) and more closely matches the stiffness of the vagina. Objective: This study assessed how pore geometry (auxetic-bowtie vs non-auxetic square or diamond) and material distribution impact the elongation, wrinkling, and porosity of EMs in response to tensile loads using finite element (FE) simulations. Study design: Nine models with varied strut dimensions were designed, maintaining constant total volume, device length, and device width. FE analysis based on a Neo-Hookean material model was used to apply 15 N uniaxial tensile loads in flat clamped and suture-like configurations. Results: For both configurations, diamond-pore membranes showed greatest elongation and porosity loss and square pore membranes showed the least. The suture-like configurations caused wrinkling that was most pronounced in bowtie-pore membranes, especially near attachment points, and least in square-pore membranes. The elongation of bowtie-pore membranes was most sensitive to material distribution, which also directly corresponded to the degree of wrinkling. Conclusion: Square pore geometry offered superior stability in response to uniaxial tension. Auxetic bowtie models showed porosity advantages, but material distribution impacted its elongation and corresponding propensity to wrinkle. These findings are critical for optimizing membrane design to minimize complications. Why this matters? (200/200 words) Current surgical meshes for pelvic organ prolapse (POP) repair remain largely adapted from hernia meshes, despite profound biomechanical and anatomical differences between the abdominal wall and the vagina. These devices are limited by their high stiffness and tendency for pore collapse and wrinkling when loaded which contributes directly to complications like chronic pain and mesh exposure. Improvements in prolapse-specific designs have not eliminated these issues, as the fundamental knitted polypropylene design remains restrictive. To address this unmet need, our work explores a vagina-centric solution-an elastomeric membrane (EM) manufactured from a material softer and more compliant than polypropylene, engineered with macropores to maintain porosity while minimizing wrinkling. Using finite element simulations, we explore the pore geometry and material distribution as key determinants of device stability. Square pore geometries consistently provided superior resistance to wrinkling and pore collapse under tensile loading, while diamond pores were highly unstable and auxetic bowtie pores, though resistant to collapse, exhibited pronounced wrinkling. These findings align with prior in vivo and ex vivo studies, underscoring the translational relevance. Importantly, this work establishes a cost-efficient computational framework for guiding next-generation device design, enabling safer, more effective prolapse repairs and providing surgeons with rational criteria for device selection.


=== TITLE ===
In silico Evaluation of an Elastomeric Membrane for Prolapse Repairs

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents original finite‑element simulation results that evaluate new membrane designs, providing novel data on wrinkling, porosity, and displacement. This constitutes new experimental data, satisfying medRxiv’s requirement.  
**CONTEXT:**  
- Abstract: “FE analysis based on a Neo‑Hookean material model was used to apply 15 N uniaxial tensile loads… Results… For both configurations, diamond‑pore membranes showed greatest elongation and porosity loss….”  
- Discussion: “In this study, computational modeling was used to inform an optimal pore geometry… We asked how changing pore geometry… impacted wrinkling behavior, displacement, and porosity of membranes.”

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents original finite‑element simulation results that quantify wrinkling, porosity change, and displacement for various pore geometries and material distributions. These computational data are new and not simply a re‑analysis of previously published studies. The work does not describe a structured clinical study protocol; it is a pre‑clinical, in silico investigation. Therefore the manuscript satisfies the requirement of containing new data.  
**CONTEXT:**  
- “In this study, computational modeling was used to inform an optimal pore geometry for the final iterations of the device.”  
- “FE analysis based on a Neo‑Hookean material model was used to apply 15 N uniaxial tensile loads in flat clamped and suture‑like configurations.”  
- “Table 1. Percent change in porosity for boundary conditions 1 and 2, relative to the undeformed configuration.”

=== ABSTRACT ===
Background: Since late 2022, the sale of most flavoured tobacco products has been prohibited in California, including menthol cigarettes. Tobacco companies responded by introducing "nonmenthol" cigarettes in which menthol was replaced with WS-3, an odorless synthetic cooling agent to elicit cooling sensations similar to menthol. Legislation enacted in 2024 banned the addition of cooling characterizing flavours in tobacco products in California. However, the industry continues to market "non-menthol" cigarettes in the state, with very similar package designs. It is unknown whether these cigarettes contain a cooling agent. Methods: Available Newport-branded "non-menthol" cigarettes were purchased in California in 2025, extracted and tested for sensory cooling activity by Ca 2+ microfluorimetry of HEK293T cells expressing the human TRPM8 cold/menthol receptor. Chemical analysis was performed by gas chromatography -mass spectrometry (GCMS). "Non-menthol" and menthol cigarettes marketed in 2023-24 served as controls. Results: Extracts from Newport-branded "non-menthol" cigarettes marketed in California in 2025 did not elicit sensory cooling activity. Chemical analysis confirmed the absence of menthol and any of the major commercial synthetic cooling agents. Conclusions: While the tobacco industry complied with California's new legislation banning cooling characterising flavours in tobacco products, this did not result in the market withdrawal of "nonmenthol" cigarettes. "Non-menthol" cigarettes continue to be marketed with package designs resembling those of former menthol cigarettes, signaling the potential presence of a characterising flavour.


=== KEYWORDS ===
menthol ban||combustible cigarettes||synthetic cooling agents|| WS-3|| characterizing flavours


=== TITLE ===
Absence of Sensory Cooling Activity and Cooling Agents from California's New "Non-Menthol" Cigarettes Marketed in 2025


=== DISCUSSION ===

Taken together, these data demonstrate that R.J. Reynolds removed sensory cooling agents from their California-marketed Newport "non-menthol" varieties. However, it is important to note that in the case of the Newport non-menthol green variety (Figure 2C), the box design and color is identical to the previous WS-3-containing version, and very similar to Newport menthol cigarettes (except the "non-menthol" label), which could continue to signal the presence of a minty and cooling characterizing flavour to people who previously smoked menthol cigarettes. R.J. Reynolds' strategy is similar to ITG Brands' for their Kool "non-menthol" cigarettes introduced in California in December 2022 that have blue/black or green/black box coloring similar to the brand's menthol cigarettes, but do not contain any cooling agent. 3 4 The industry's distinctive choice of package colouring is another common strategy to manipulate consumer expectations about the flavour and strength of tobacco products. 11 In June 2023, California regulators raised concerns about the misleading package design and coloring of these Kool "non-menthol" cigarettes. 12 Similar concerns should be raised about the new Newport non-menthol green variety introduced in 2025 that, while not containing a banned characterising flavour, continues to signal that a characterising flavour may be present. A Newport menthol cigarettes box purchased in North Carolina in 2023. B Newport "Non-Menthol" green box purchased in California in 2023, in cellophane wrapper with white letters on gold background. C Newport "Non-Menthol" Box (green) purchased in California in March 2025, in cellophane wrapper with "New Non-Menthol" printed in white letters on black background, and "Earn Rewards" printed in black letters on gold background and with arrow pattern. D Newport "Non-Menthol" Reserve Box (red/black) purchased in California in March 2025. E Ingredient list of Newport Non-Menthol Green Box (shown in B), copied from the 2023 R.J. Reynolds commercial integrity website, with WS-3 (n-Ethyl-p-Menthane-3-Carboxamide) highlighted. F Ingredient list of 2025 Newport Non-Menthol Box (shown in C), copied from the 2025 R.J. Reynolds commercial integrity website. California in 2023 and 2025, compared to Newport menthol cigarettes. Dose-response analysis of human TRPM8 cold/menthol receptor-mediated Ca 2+ -influx, upon superfusion of cells with dilution series of extracts from Newport brand cigarettes. Green: Nonmenthol green box, purchased in CA in 2023; Purple: Menthol green box, purchased in North Carolina in 2023; Red: New non-menthol green box, purchased in CA in 2025; Black: New nonmenthol reserve, purchased in CA in 2025. The increase in intracellular Ca 2+ , measured as fluorescence units (F max -F 0 ), was normalized to the Ca 2+ -response elicited by a saturating concentration of agonist L-menthol (1 mM; solid black triangle). Response to a saturating concentration of WS-3 (100 µM; solid black circle) and vehicle control (orange diamond) shown for comparison. 1X dilution is defined as the extract of one tobacco rod contents in 50 mL assay buffer, and 10X is 10-fold dilution thereof. A B C D E F Figure 2
Figure 1: New "non-menthol" cigarettes introduced in California in 2025
Figure 2: Sensory cooling activity of Newport "non-menthol" cigarettes introduced in
Figure 1


=== INTRODUCTION ===

The funding organizations had no role in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; the preparation, review, or approval of the manuscript; nor in the decision to submit the manuscript for publication. The content is solely the responsibility of the authors and does not necessarily represent the views of National Institutes of Health or the Food and Drug Administration.

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

Objective  
Confirm whether the manuscript qualifies as a research paper or a systematic review based on structural and methodological characteristics.

Instructions  
1. Evaluate the manuscript for the following:
 - A research paper presents original research findings based on experiments, observations, or analyses conducted by the authors.
 - A systematic review provides a structured review of prior research following a clear methodology (e.g., inclusion/exclusion criteria, data synthesis).
2. For systematic reviews, look for indicators such as:
 - Mention of search strategies, search terms, or databases used.
 - Use of established frameworks like PRISMA, PICO, or equivalent (optional but useful indicators).
3. Check for presence of standard sections:
 - Abstract, Introduction, Methods, Results, Discussion/Conclusion.
 - Accept variants such as Background, Materials and Methods, Findings, or Conclusions.
4. Look for language suggesting original research or systematic methodology, such as:
 - “we conducted experiments,” “our findings show,” “we systematically reviewed,” “studies were selected based on.”
5. Mark the manuscript as FAIL if it lacks structural and methodological indicators of original research or systematic review.
- Narrative summaries, essays, or theory pieces without structured methodology should be marked FAIL.
6.  If it IS a research paper or a systematic review, mark as PASS. If it is NOT a research paper or a systematic review, mark as FAIL.

Output Format
- PASS / FAIL
- Detailed Explanation
- Context From the Paper

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

**SPECIFIC CHECK:**
Objective  
Confirm whether the manuscript qualifies as a research paper or a systematic review based on structural and methodological characteristics.

Instructions  
1. Evaluate the manuscript for the following:
 - A research paper presents original research findings based on experiments, observations, or analyses conducted by the authors.
 - A systematic review provides a structured review of prior research following a clear methodology (e.g., inclusion/exclusion criteria, data synthesis).
2. For systematic reviews, look for indicators such as:
 - Mention of search strategies, search terms, or databases used.
 - Use of established frameworks like PRISMA, PICO, or equivalent (optional but useful indicators).
3. Check for presence of standard sections:
 - Abstract, Introduction, Methods, Results, Discussion/Conclusion.
 - Accept variants such as Background, Materials and Methods, Findings, or Conclusions.
4. Look for language suggesting original research or systematic methodology, such as:
 - “we conducted experiments,” “our findings show,” “we systematically reviewed,” “studies were selected based on.”
5. Mark the manuscript as FAIL if it lacks structural and methodological indicators of original research or systematic review.
- Narrative summaries, essays, or theory pieces without structured methodology should be marked FAIL.
6.  If it IS a research paper or a systematic review, mark as PASS. If it is NOT a research paper or a systematic review, mark as FAIL.

Output Format
- PASS / FAIL
- Detailed Explanation
- Context From the Paper

**DOCUMENT CONTENT TO ANALYZE:**

=== ABSTRACT ===
Background: Since late 2022, the sale of most flavoured tobacco products has been prohibited in California, including menthol cigarettes. Tobacco companies responded by introducing "nonmenthol" cigarettes in which menthol was replaced with WS-3, an odorless synthetic cooling agent to elicit cooling sensations similar to menthol. Legislation enacted in 2024 banned the addition of cooling characterizing flavours in tobacco products in California. However, the industry continues to market "non-menthol" cigarettes in the state, with very similar package designs. It is unknown whether these cigarettes contain a cooling agent. Methods: Available Newport-branded "non-menthol" cigarettes were purchased in California in 2025, extracted and tested for sensory cooling activity by Ca 2+ microfluorimetry of HEK293T cells expressing the human TRPM8 cold/menthol receptor. Chemical analysis was performed by gas chromatography -mass spectrometry (GCMS). "Non-menthol" and menthol cigarettes marketed in 2023-24 served as controls. Results: Extracts from Newport-branded "non-menthol" cigarettes marketed in California in 2025 did not elicit sensory cooling activity. Chemical analysis confirmed the absence of menthol and any of the major commercial synthetic cooling agents. Conclusions: While the tobacco industry complied with California's new legislation banning cooling characterising flavours in tobacco products, this did not result in the market withdrawal of "nonmenthol" cigarettes. "Non-menthol" cigarettes continue to be marketed with package designs resembling those of former menthol cigarettes, signaling the potential presence of a characterising flavour.


=== KEYWORDS ===
menthol ban||combustible cigarettes||synthetic cooling agents|| WS-3|| characterizing flavours


=== TITLE ===
Absence of Sensory Cooling Activity and Cooling Agents from California's New "Non-Menthol" Cigarettes Marketed in 2025


=== DISCUSSION ===

Taken together, these data demonstrate that R.J. Reynolds removed sensory cooling agents from their California-marketed Newport "non-menthol" varieties. However, it is important to note that in the case of the Newport non-menthol green variety (Figure 2C), the box design and color is identical to the previous WS-3-containing version, and very similar to Newport menthol cigarettes (except the "non-menthol" label), which could continue to signal the presence of a minty and cooling characterizing flavour to people who previously smoked menthol cigarettes. R.J. Reynolds' strategy is similar to ITG Brands' for their Kool "non-menthol" cigarettes introduced in California in December 2022 that have blue/black or green/black box coloring similar to the brand's menthol cigarettes, but do not contain any cooling agent. 3 4 The industry's distinctive choice of package colouring is another common strategy to manipulate consumer expectations about the flavour and strength of tobacco products. 11 In June 2023, California regulators raised concerns about the misleading package design and coloring of these Kool "non-menthol" cigarettes. 12 Similar concerns should be raised about the new Newport non-menthol green variety introduced in 2025 that, while not containing a banned characterising flavour, continues to signal that a characterising flavour may be present. A Newport menthol cigarettes box purchased in North Carolina in 2023. B Newport "Non-Menthol" green box purchased in California in 2023, in cellophane wrapper with white letters on gold background. C Newport "Non-Menthol" Box (green) purchased in California in March 2025, in cellophane wrapper with "New Non-Menthol" printed in white letters on black background, and "Earn Rewards" printed in black letters on gold background and with arrow pattern. D Newport "Non-Menthol" Reserve Box (red/black) purchased in California in March 2025. E Ingredient list of Newport Non-Menthol Green Box (shown in B), copied from the 2023 R.J. Reynolds commercial integrity website, with WS-3 (n-Ethyl-p-Menthane-3-Carboxamide) highlighted. F Ingredient list of 2025 Newport Non-Menthol Box (shown in C), copied from the 2025 R.J. Reynolds commercial integrity website. California in 2023 and 2025, compared to Newport menthol cigarettes. Dose-response analysis of human TRPM8 cold/menthol receptor-mediated Ca 2+ -influx, upon superfusion of cells with dilution series of extracts from Newport brand cigarettes. Green: Nonmenthol green box, purchased in CA in 2023; Purple: Menthol green box, purchased in North Carolina in 2023; Red: New non-menthol green box, purchased in CA in 2025; Black: New nonmenthol reserve, purchased in CA in 2025. The increase in intracellular Ca 2+ , measured as fluorescence units (F max -F 0 ), was normalized to the Ca 2+ -response elicited by a saturating concentration of agonist L-menthol (1 mM; solid black triangle). Response to a saturating concentration of WS-3 (100 µM; solid black circle) and vehicle control (orange diamond) shown for comparison. 1X dilution is defined as the extract of one tobacco rod contents in 50 mL assay buffer, and 10X is 10-fold dilution thereof. A B C D E F Figure 2
Figure 1: New "non-menthol" cigarettes introduced in California in 2025
Figure 2: Sensory cooling activity of Newport "non-menthol" cigarettes introduced in
Figure 1


=== INTRODUCTION ===

The funding organizations had no role in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; the preparation, review, or approval of the manuscript; nor in the decision to submit the manuscript for publication. The content is solely the responsibility of the authors and does not necessarily represent the views of National Institutes of Health or the Food and Drug Administration.

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents original experimental work—collecting cigarette samples, performing Ca²⁺ microfluorimetry and GC‑MS analysis—to test for sensory cooling activity and cooling agents. It follows the typical structure of a research paper (Abstract, Introduction, Methods/Results implied in the abstract, Discussion). No systematic review methodology is described.  
**CONTEXT:**  
- Abstract: “Methods: Available Newport‑branded “non‑menthol” cigarettes were purchased… extracted and tested for sensory cooling activity… Chemical analysis was performed by gas chromatography‑mass spectrometry (GCMS).”  
- Discussion: “Taken together, these data demonstrate that R.J. Reynolds removed sensory cooling agents…”  
- Title and keywords indicate a specific experimental investigation.

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents original experimental work—collecting cigarette samples, performing biochemical assays (TRPM8 Ca²⁺ microfluorimetry) and GC‑MS chemical analysis—to test for cooling agents. It follows the typical research paper structure with an abstract, introduction, methods (described in the abstract and discussion), results, and discussion.  
**CONTEXT:**  
- Abstract: “Methods: Available Newport‑branded “non‑menthol” cigarettes were purchased… extracted and tested for sensory cooling activity… Chemical analysis was performed by gas chromatography‑mass spectrometry (GCMS).”  
- Discussion: “Taken together, these data demonstrate that R.J. Reynolds removed sensory cooling agents… Dose‑response analysis of human TRPM8 cold/menthol receptor‑mediated Ca²⁺‑influx….”

=== DISCUSSION ===

Taken together, these data demonstrate that R.J. Reynolds removed sensory cooling agents from their California-marketed Newport "non-menthol" varieties. However, it is important to note that in the case of the Newport non-menthol green variety (Figure 2C), the box design and color is identical to the previous WS-3-containing version, and very similar to Newport menthol cigarettes (except the "non-menthol" label), which could continue to signal the presence of a minty and cooling characterizing flavour to people who previously smoked menthol cigarettes. R.J. Reynolds' strategy is similar to ITG Brands' for their Kool "non-menthol" cigarettes introduced in California in December 2022 that have blue/black or green/black box coloring similar to the brand's menthol cigarettes, but do not contain any cooling agent. 3 4 The industry's distinctive choice of package colouring is another common strategy to manipulate consumer expectations about the flavour and strength of tobacco products. 11 In June 2023, California regulators raised concerns about the misleading package design and coloring of these Kool "non-menthol" cigarettes. 12 Similar concerns should be raised about the new Newport non-menthol green variety introduced in 2025 that, while not containing a banned characterising flavour, continues to signal that a characterising flavour may be present. A Newport menthol cigarettes box purchased in North Carolina in 2023. B Newport "Non-Menthol" green box purchased in California in 2023, in cellophane wrapper with white letters on gold background. C Newport "Non-Menthol" Box (green) purchased in California in March 2025, in cellophane wrapper with "New Non-Menthol" printed in white letters on black background, and "Earn Rewards" printed in black letters on gold background and with arrow pattern. D Newport "Non-Menthol" Reserve Box (red/black) purchased in California in March 2025. E Ingredient list of Newport Non-Menthol Green Box (shown in B), copied from the 2023 R.J. Reynolds commercial integrity website, with WS-3 (n-Ethyl-p-Menthane-3-Carboxamide) highlighted. F Ingredient list of 2025 Newport Non-Menthol Box (shown in C), copied from the 2025 R.J. Reynolds commercial integrity website. California in 2023 and 2025, compared to Newport menthol cigarettes. Dose-response analysis of human TRPM8 cold/menthol receptor-mediated Ca 2+ -influx, upon superfusion of cells with dilution series of extracts from Newport brand cigarettes. Green: Nonmenthol green box, purchased in CA in 2023; Purple: Menthol green box, purchased in North Carolina in 2023; Red: New non-menthol green box, purchased in CA in 2025; Black: New nonmenthol reserve, purchased in CA in 2025. The increase in intracellular Ca 2+ , measured as fluorescence units (F max -F 0 ), was normalized to the Ca 2+ -response elicited by a saturating concentration of agonist L-menthol (1 mM; solid black triangle). Response to a saturating concentration of WS-3 (100 µM; solid black circle) and vehicle control (orange diamond) shown for comparison. 1X dilution is defined as the extract of one tobacco rod contents in 50 mL assay buffer, and 10X is 10-fold dilution thereof. A B C D E F Figure 2
Figure 1: New "non-menthol" cigarettes introduced in California in 2025
Figure 2: Sensory cooling activity of Newport "non-menthol" cigarettes introduced in
Figure 1


=== INTRODUCTION ===

The funding organizations had no role in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; the preparation, review, or approval of the manuscript; nor in the decision to submit the manuscript for publication. The content is solely the responsibility of the authors and does not necessarily represent the views of National Institutes of Health or the Food and Drug Administration.


=== ABSTRACT ===
Background: Since late 2022, the sale of most flavoured tobacco products has been prohibited in California, including menthol cigarettes. Tobacco companies responded by introducing "nonmenthol" cigarettes in which menthol was replaced with WS-3, an odorless synthetic cooling agent to elicit cooling sensations similar to menthol. Legislation enacted in 2024 banned the addition of cooling characterizing flavours in tobacco products in California. However, the industry continues to market "non-menthol" cigarettes in the state, with very similar package designs. It is unknown whether these cigarettes contain a cooling agent. Methods: Available Newport-branded "non-menthol" cigarettes were purchased in California in 2025, extracted and tested for sensory cooling activity by Ca 2+ microfluorimetry of HEK293T cells expressing the human TRPM8 cold/menthol receptor. Chemical analysis was performed by gas chromatography -mass spectrometry (GCMS). "Non-menthol" and menthol cigarettes marketed in 2023-24 served as controls. Results: Extracts from Newport-branded "non-menthol" cigarettes marketed in California in 2025 did not elicit sensory cooling activity. Chemical analysis confirmed the absence of menthol and any of the major commercial synthetic cooling agents. Conclusions: While the tobacco industry complied with California's new legislation banning cooling characterising flavours in tobacco products, this did not result in the market withdrawal of "nonmenthol" cigarettes. "Non-menthol" cigarettes continue to be marketed with package designs resembling those of former menthol cigarettes, signaling the potential presence of a characterising flavour.


=== KEYWORDS ===
menthol ban||combustible cigarettes||synthetic cooling agents|| WS-3|| characterizing flavours


=== TITLE ===
Absence of Sensory Cooling Activity and Cooling Agents from California's New "Non-Menthol" Cigarettes Marketed in 2025

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

Objective  
Confirm whether the manuscript includes new data or qualifies as a clinical research protocol, based on content required by medRxiv.

Instructions 
1. Analyze the manuscript for the presence of either:
	- New data from experiments, observations, clinical trials, or simulations, even if based on previously published sources (e.g., meta-analyses with new synthesis).
	- For a medRxiv submission, check whether the submission is a clinical research protocol, defined as a structured plan for a clinical study.
2. Treat meta-analyses and data syntheses from prior studies as valid forms of ""data"" for this check, if they involve original re-analysis or synthesis.
3. To confirm presence of new data:
	- Review tables, figures, or data presentations that are not directly reused from prior published work.
	- Look for explicit mentions of new findings or original analysis.
4. To confirm a clinical research protocol:
	- Check for structured information on study objectives, design, methods, eligibility criteria, or trial phases.
	- Trial registration numbers or ethics review statements are useful indicators.
5. Mark the manuscript as FAIL if neither new data nor clinical protocol indicators are found.
	- If the content is ambiguous or borderline, mark as FAIL with a clear explanation so that a human screener can review the context.

Output
1. Provide a PASS or FAIL result
	- Has New Data Or Is A Clinical Research Protocol: PASS
	- Does Not Have New Data Or A Clinical Research Protocol: FAIL
2. Provide a Detailed Explanation to evidence the PASS or FAIL result
3. Provide the context From the Paper to evidence the PASS or FAIL result

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

**SPECIFIC CHECK:**
Objective  
Confirm whether the manuscript includes new data or qualifies as a clinical research protocol, based on content required by medRxiv.

Instructions 
1. Analyze the manuscript for the presence of either:
	- New data from experiments, observations, clinical trials, or simulations, even if based on previously published sources (e.g., meta-analyses with new synthesis).
	- For a medRxiv submission, check whether the submission is a clinical research protocol, defined as a structured plan for a clinical study.
2. Treat meta-analyses and data syntheses from prior studies as valid forms of ""data"" for this check, if they involve original re-analysis or synthesis.
3. To confirm presence of new data:
	- Review tables, figures, or data presentations that are not directly reused from prior published work.
	- Look for explicit mentions of new findings or original analysis.
4. To confirm a clinical research protocol:
	- Check for structured information on study objectives, design, methods, eligibility criteria, or trial phases.
	- Trial registration numbers or ethics review statements are useful indicators.
5. Mark the manuscript as FAIL if neither new data nor clinical protocol indicators are found.
	- If the content is ambiguous or borderline, mark as FAIL with a clear explanation so that a human screener can review the context.

Output
1. Provide a PASS or FAIL result
	- Has New Data Or Is A Clinical Research Protocol: PASS
	- Does Not Have New Data Or A Clinical Research Protocol: FAIL
2. Provide a Detailed Explanation to evidence the PASS or FAIL result
3. Provide the context From the Paper to evidence the PASS or FAIL result



**DOCUMENT CONTENT TO ANALYZE:**

=== DISCUSSION ===

Taken together, these data demonstrate that R.J. Reynolds removed sensory cooling agents from their California-marketed Newport "non-menthol" varieties. However, it is important to note that in the case of the Newport non-menthol green variety (Figure 2C), the box design and color is identical to the previous WS-3-containing version, and very similar to Newport menthol cigarettes (except the "non-menthol" label), which could continue to signal the presence of a minty and cooling characterizing flavour to people who previously smoked menthol cigarettes. R.J. Reynolds' strategy is similar to ITG Brands' for their Kool "non-menthol" cigarettes introduced in California in December 2022 that have blue/black or green/black box coloring similar to the brand's menthol cigarettes, but do not contain any cooling agent. 3 4 The industry's distinctive choice of package colouring is another common strategy to manipulate consumer expectations about the flavour and strength of tobacco products. 11 In June 2023, California regulators raised concerns about the misleading package design and coloring of these Kool "non-menthol" cigarettes. 12 Similar concerns should be raised about the new Newport non-menthol green variety introduced in 2025 that, while not containing a banned characterising flavour, continues to signal that a characterising flavour may be present. A Newport menthol cigarettes box purchased in North Carolina in 2023. B Newport "Non-Menthol" green box purchased in California in 2023, in cellophane wrapper with white letters on gold background. C Newport "Non-Menthol" Box (green) purchased in California in March 2025, in cellophane wrapper with "New Non-Menthol" printed in white letters on black background, and "Earn Rewards" printed in black letters on gold background and with arrow pattern. D Newport "Non-Menthol" Reserve Box (red/black) purchased in California in March 2025. E Ingredient list of Newport Non-Menthol Green Box (shown in B), copied from the 2023 R.J. Reynolds commercial integrity website, with WS-3 (n-Ethyl-p-Menthane-3-Carboxamide) highlighted. F Ingredient list of 2025 Newport Non-Menthol Box (shown in C), copied from the 2025 R.J. Reynolds commercial integrity website. California in 2023 and 2025, compared to Newport menthol cigarettes. Dose-response analysis of human TRPM8 cold/menthol receptor-mediated Ca 2+ -influx, upon superfusion of cells with dilution series of extracts from Newport brand cigarettes. Green: Nonmenthol green box, purchased in CA in 2023; Purple: Menthol green box, purchased in North Carolina in 2023; Red: New non-menthol green box, purchased in CA in 2025; Black: New nonmenthol reserve, purchased in CA in 2025. The increase in intracellular Ca 2+ , measured as fluorescence units (F max -F 0 ), was normalized to the Ca 2+ -response elicited by a saturating concentration of agonist L-menthol (1 mM; solid black triangle). Response to a saturating concentration of WS-3 (100 µM; solid black circle) and vehicle control (orange diamond) shown for comparison. 1X dilution is defined as the extract of one tobacco rod contents in 50 mL assay buffer, and 10X is 10-fold dilution thereof. A B C D E F Figure 2
Figure 1: New "non-menthol" cigarettes introduced in California in 2025
Figure 2: Sensory cooling activity of Newport "non-menthol" cigarettes introduced in
Figure 1


=== INTRODUCTION ===

The funding organizations had no role in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; the preparation, review, or approval of the manuscript; nor in the decision to submit the manuscript for publication. The content is solely the responsibility of the authors and does not necessarily represent the views of National Institutes of Health or the Food and Drug Administration.


=== ABSTRACT ===
Background: Since late 2022, the sale of most flavoured tobacco products has been prohibited in California, including menthol cigarettes. Tobacco companies responded by introducing "nonmenthol" cigarettes in which menthol was replaced with WS-3, an odorless synthetic cooling agent to elicit cooling sensations similar to menthol. Legislation enacted in 2024 banned the addition of cooling characterizing flavours in tobacco products in California. However, the industry continues to market "non-menthol" cigarettes in the state, with very similar package designs. It is unknown whether these cigarettes contain a cooling agent. Methods: Available Newport-branded "non-menthol" cigarettes were purchased in California in 2025, extracted and tested for sensory cooling activity by Ca 2+ microfluorimetry of HEK293T cells expressing the human TRPM8 cold/menthol receptor. Chemical analysis was performed by gas chromatography -mass spectrometry (GCMS). "Non-menthol" and menthol cigarettes marketed in 2023-24 served as controls. Results: Extracts from Newport-branded "non-menthol" cigarettes marketed in California in 2025 did not elicit sensory cooling activity. Chemical analysis confirmed the absence of menthol and any of the major commercial synthetic cooling agents. Conclusions: While the tobacco industry complied with California's new legislation banning cooling characterising flavours in tobacco products, this did not result in the market withdrawal of "nonmenthol" cigarettes. "Non-menthol" cigarettes continue to be marketed with package designs resembling those of former menthol cigarettes, signaling the potential presence of a characterising flavour.


=== KEYWORDS ===
menthol ban||combustible cigarettes||synthetic cooling agents|| WS-3|| characterizing flavours


=== TITLE ===
Absence of Sensory Cooling Activity and Cooling Agents from California's New "Non-Menthol" Cigarettes Marketed in 2025

**RESULT:** PASS  
**EXPLANATION:** The manuscript reports original experimental work: cigarettes were purchased, extracted, and tested for sensory cooling activity using Ca²⁺ microfluorimetry, followed by GC‑MS chemical analysis. The results (e.g., “Extracts from Newport‑branded “non‑menthol” cigarettes marketed in California in 2025 did not elicit sensory cooling activity” and the presentation of new figures) constitute new data.  
**CONTEXT:**  
- “Methods: Available Newport‑branded “non‑menthol” cigarettes were purchased in California in 2025, extracted and tested for sensory cooling activity by Ca²⁺ microfluorimetry of HEK293T cells expressing the human TRPM8 cold/menthol receptor. Chemical analysis was performed by gas chromatography‑mass spectrometry (GCMS).”  
- “Results: Extracts from Newport‑branded “non‑menthol” cigarettes marketed in California in 2025 did not elicit sensory cooling activity. Chemical analysis confirmed the absence of menthol and any of the major commercial synthetic cooling agents.”  
- Figures 1 and 2 display new experimental data (e.g., dose‑response curves, ingredient lists).

**RESULT:** PASS  
**EXPLANATION:** The manuscript reports original experimental work: cigarettes were purchased, extracted, and assayed for sensory cooling activity using Ca²⁺ microfluorimetry and GC‑MS chemical analysis. These new data are presented in figures and the results section, indicating that the study contains novel findings rather than merely a protocol.  
**CONTEXT:**  
- Abstract, Methods: “Available Newport‑branded 'non‑menthol' cigarettes were purchased… extracted and tested for sensory cooling activity by Ca²⁺ microfluorimetry of HEK293T cells… Chemical analysis was performed by gas chromatography‑mass spectrometry (GCMS).”  
- Abstract, Results: “Extracts from Newport‑branded 'non‑menthol' cigarettes marketed in California in 2025 did not elicit sensory cooling activity. Chemical analysis confirmed the absence of menthol and any of the major commercial synthetic cooling agents.”  
- Discussion and figure captions describe new dose‑response data and chemical analyses (e.g., Figure 2, dose‑response analysis of human TRPM8 receptor‑mediated Ca²⁺ influx).

=== ABSTRACT ===
Background: Neurological manifestations of cancer can have multiple causes. Among these, paraneoplastic syndromes are disorders that arise from immune system responses, either as a direct consequence of the cancer itself or because of its treatment. While rare, paraneoplastic neurological syndromes can occur in association with ovarian tumours. Paraneoplastic cerebellar degeneration (PCD) often linked to the presence of onconeural antibodies may occur alongside ovarian neoplasms. Case presentation: We report the case of a 64-year-old woman who experienced rapidly progressive cerebellar dysfunction over a five-day period. Subsequent investigations revealed a malignant ovarian tumour, and her worsening neurological condition was attributed to a paraneoplastic neurological syndrome. Conclusion: This case underscores the importance of considering paraneoplastic cerebellar degeneration in the differential diagnosis of acute cerebellar syndromes, especially when initial neuroimaging rules out acute stroke. Notably, neurological symptoms often precede the diagnosis of an underlying malignancy. Therefore, the early recognition of a paraneoplastic syndrome should prompt an urgent and thorough search for an occult tumour


=== KEYWORDS ===
Paraneoplastic cerebellar degeneration||ovarian cancer||onconeuronal antibodies|| Stroke mimic


=== TITLE ===
Paraneoplastic cerebellar degeneration masquerading as acute stroke in a patient with ovarian tumour


=== DISCUSSION ===

Paraneoplastic neurological syndromes (PNS) are rare, immune-mediated disorders that reflect the remote effects of systemic malignancies. These syndromes are usually associated with onconeural antibodies that target intracellular antigens shared by tumour cells and neurons. These antibodies can serve as diagnostic biomarkers and assist in identifying and localizing the underlying malignancy, thereby facilitating early and targeted therapeutic interventions [1,2].
One uncommon subtype of PNS is paraneoplastic cerebellar degeneration (PCD), which is characterized by subacute cerebellar dysfunction due to autoimmunemediated destruction of Purkinje cells. Clinically, PCD manifests as vertigo, nystagmus, diplopia, dysarthria, limb and truncal ataxia, and progressive gait instability. The condition often progresses rapidly, leading to significant neurological disability. Unfortunately, the prognosis is poor, as cerebellar damage is frequently irreversible, even after effective oncological treatment [1,3].
PCD has been associated with various onconeural antibodies, including anti-Yo, anti-Ri, anti-Tr, anti-Zic4, and anti-glutamic acid decarboxylase (GAD). Among these, anti-Yo antibodies are most frequently linked to gynaecologic and breast malignancies [3,4]. While the presence of onconeural antibodies supports the diagnosis of PNS, their absence does not rule it out, as seronegative cases are not uncommon [2,5]. This highlights the importance of a comprehensive clinical evaluation alongside ancillary testing.
We present the case of a 64-year-old female who exhibited acute-onset cerebellar symptoms without any brainstem pathology on neuroimaging. Further investigations, including spinal MRI, cerebrospinal fluid (CSF) analysis, a paraneoplastic antibody panel, tumour markers, and contrast-enhanced CT of the thorax, abdomen, and pelvis, identified an underlying ovarian carcinoma. The patient tested positive for anti-Yo antibodies, confirming a diagnosis of PCD. Notably, her neurological symptoms appeared before the diagnosis of malignancy. This temporal relationship aligns with existing literature, which indicates that PNS, particularly PCD, may precede the clinical detection of cancer by several months to years. In approximately 15% of reported cases, cerebellar symptoms emerged up to three years before tumour diagnosis [2,3]. Importantly, our patient showed no clinical signs or symptoms indicative of ovarian pathology at presentation, reinforcing the idea that PCD can be the initial and sole manifestation of an occult neoplasm.
Diagnostic evaluation of suspected PCD should be guided by clinical presentation and serological findings. Neuroimaging may reveal cerebellar atrophy in advanced cases, although early imaging can appear normal [1,3]. CSF analysis often shows mild lymphocytic pleocytosis, elevated protein levels, and unpaired oligoclonal bands, while cytological examination typically does not identify malignant cells [1,5]. Elevated tumour markers can provide additional diagnostic insights, particularly in cases involving gynaecologic or gastrointestinal malignancies.
Management of PCD necessitates prompt recognition and treatment of the underlying malignancy, which is critical for patient care. Although PCD often does not respond to immunotherapy-especially in cases positive for anti-Yo antibodiesthere have been reports of clinical stabilization or modest improvement with immunosuppressive agents such as corticosteroids, intravenous immunoglobulin (IVIG), and plasmapheresis [3,5].
In our case, the initiation of high-dose dexamethasone resulted in mild symptomatic improvement. Surgical removal of the tumour has been reported to lead to stabilization or improvement of neurological symptoms in select cases [3]. Unfortunately, our patient developed postoperative urinary sepsis following debulking surgery and ultimately succumbed to this complication despite aggressive medical management. This case highlights the importance of considering PCD in the differential diagnosis of subacute cerebellar syndromes, especially when routine neuroimaging reveals no abnormalities, and vascular aetiologies have been excluded. A high index of suspicion, combined with targeted serological and oncological investigations, is essential for the early diagnosis and timely intervention in these rare but clinically significant syndromes.


=== INTRODUCTION ===

Paraneoplastic cerebellar degeneration (PCD) is a rare form of paraneoplastic neurological syndromes (PNS) that is rarely encountered in clinical practice. It affects less than 1% of patients with cancer and is most associated with cancers of the ovary, uterus, breast, small-cell lung carcinoma, and Hodgkin's lymphoma.
We present a case of a patient with an underlying ovarian tumour who experienced progressively worsening cerebellar dysfunction due to PCD. The initial presentation closely resembled an acute posterior circulation stroke, emphasizing the diagnostic challenges and the necessity of considering paraneoplastic causes in such clinical situations

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

Objective  
Confirm whether the manuscript qualifies as a research paper or a systematic review based on structural and methodological characteristics.

Instructions  
1. Evaluate the manuscript for the following:
 - A research paper presents original research findings based on experiments, observations, or analyses conducted by the authors.
 - A systematic review provides a structured review of prior research following a clear methodology (e.g., inclusion/exclusion criteria, data synthesis).
2. For systematic reviews, look for indicators such as:
 - Mention of search strategies, search terms, or databases used.
 - Use of established frameworks like PRISMA, PICO, or equivalent (optional but useful indicators).
3. Check for presence of standard sections:
 - Abstract, Introduction, Methods, Results, Discussion/Conclusion.
 - Accept variants such as Background, Materials and Methods, Findings, or Conclusions.
4. Look for language suggesting original research or systematic methodology, such as:
 - “we conducted experiments,” “our findings show,” “we systematically reviewed,” “studies were selected based on.”
5. Mark the manuscript as FAIL if it lacks structural and methodological indicators of original research or systematic review.
- Narrative summaries, essays, or theory pieces without structured methodology should be marked FAIL.
6.  If it IS a research paper or a systematic review, mark as PASS. If it is NOT a research paper or a systematic review, mark as FAIL.

Output Format
- PASS / FAIL
- Detailed Explanation
- Context From the Paper

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

**SPECIFIC CHECK:**
Objective  
Confirm whether the manuscript qualifies as a research paper or a systematic review based on structural and methodological characteristics.

Instructions  
1. Evaluate the manuscript for the following:
 - A research paper presents original research findings based on experiments, observations, or analyses conducted by the authors.
 - A systematic review provides a structured review of prior research following a clear methodology (e.g., inclusion/exclusion criteria, data synthesis).
2. For systematic reviews, look for indicators such as:
 - Mention of search strategies, search terms, or databases used.
 - Use of established frameworks like PRISMA, PICO, or equivalent (optional but useful indicators).
3. Check for presence of standard sections:
 - Abstract, Introduction, Methods, Results, Discussion/Conclusion.
 - Accept variants such as Background, Materials and Methods, Findings, or Conclusions.
4. Look for language suggesting original research or systematic methodology, such as:
 - “we conducted experiments,” “our findings show,” “we systematically reviewed,” “studies were selected based on.”
5. Mark the manuscript as FAIL if it lacks structural and methodological indicators of original research or systematic review.
- Narrative summaries, essays, or theory pieces without structured methodology should be marked FAIL.
6.  If it IS a research paper or a systematic review, mark as PASS. If it is NOT a research paper or a systematic review, mark as FAIL.

Output Format
- PASS / FAIL
- Detailed Explanation
- Context From the Paper

**DOCUMENT CONTENT TO ANALYZE:**

=== ABSTRACT ===
Background: Neurological manifestations of cancer can have multiple causes. Among these, paraneoplastic syndromes are disorders that arise from immune system responses, either as a direct consequence of the cancer itself or because of its treatment. While rare, paraneoplastic neurological syndromes can occur in association with ovarian tumours. Paraneoplastic cerebellar degeneration (PCD) often linked to the presence of onconeural antibodies may occur alongside ovarian neoplasms. Case presentation: We report the case of a 64-year-old woman who experienced rapidly progressive cerebellar dysfunction over a five-day period. Subsequent investigations revealed a malignant ovarian tumour, and her worsening neurological condition was attributed to a paraneoplastic neurological syndrome. Conclusion: This case underscores the importance of considering paraneoplastic cerebellar degeneration in the differential diagnosis of acute cerebellar syndromes, especially when initial neuroimaging rules out acute stroke. Notably, neurological symptoms often precede the diagnosis of an underlying malignancy. Therefore, the early recognition of a paraneoplastic syndrome should prompt an urgent and thorough search for an occult tumour


=== KEYWORDS ===
Paraneoplastic cerebellar degeneration||ovarian cancer||onconeuronal antibodies|| Stroke mimic


=== TITLE ===
Paraneoplastic cerebellar degeneration masquerading as acute stroke in a patient with ovarian tumour


=== DISCUSSION ===

Paraneoplastic neurological syndromes (PNS) are rare, immune-mediated disorders that reflect the remote effects of systemic malignancies. These syndromes are usually associated with onconeural antibodies that target intracellular antigens shared by tumour cells and neurons. These antibodies can serve as diagnostic biomarkers and assist in identifying and localizing the underlying malignancy, thereby facilitating early and targeted therapeutic interventions [1,2].
One uncommon subtype of PNS is paraneoplastic cerebellar degeneration (PCD), which is characterized by subacute cerebellar dysfunction due to autoimmunemediated destruction of Purkinje cells. Clinically, PCD manifests as vertigo, nystagmus, diplopia, dysarthria, limb and truncal ataxia, and progressive gait instability. The condition often progresses rapidly, leading to significant neurological disability. Unfortunately, the prognosis is poor, as cerebellar damage is frequently irreversible, even after effective oncological treatment [1,3].
PCD has been associated with various onconeural antibodies, including anti-Yo, anti-Ri, anti-Tr, anti-Zic4, and anti-glutamic acid decarboxylase (GAD). Among these, anti-Yo antibodies are most frequently linked to gynaecologic and breast malignancies [3,4]. While the presence of onconeural antibodies supports the diagnosis of PNS, their absence does not rule it out, as seronegative cases are not uncommon [2,5]. This highlights the importance of a comprehensive clinical evaluation alongside ancillary testing.
We present the case of a 64-year-old female who exhibited acute-onset cerebellar symptoms without any brainstem pathology on neuroimaging. Further investigations, including spinal MRI, cerebrospinal fluid (CSF) analysis, a paraneoplastic antibody panel, tumour markers, and contrast-enhanced CT of the thorax, abdomen, and pelvis, identified an underlying ovarian carcinoma. The patient tested positive for anti-Yo antibodies, confirming a diagnosis of PCD. Notably, her neurological symptoms appeared before the diagnosis of malignancy. This temporal relationship aligns with existing literature, which indicates that PNS, particularly PCD, may precede the clinical detection of cancer by several months to years. In approximately 15% of reported cases, cerebellar symptoms emerged up to three years before tumour diagnosis [2,3]. Importantly, our patient showed no clinical signs or symptoms indicative of ovarian pathology at presentation, reinforcing the idea that PCD can be the initial and sole manifestation of an occult neoplasm.
Diagnostic evaluation of suspected PCD should be guided by clinical presentation and serological findings. Neuroimaging may reveal cerebellar atrophy in advanced cases, although early imaging can appear normal [1,3]. CSF analysis often shows mild lymphocytic pleocytosis, elevated protein levels, and unpaired oligoclonal bands, while cytological examination typically does not identify malignant cells [1,5]. Elevated tumour markers can provide additional diagnostic insights, particularly in cases involving gynaecologic or gastrointestinal malignancies.
Management of PCD necessitates prompt recognition and treatment of the underlying malignancy, which is critical for patient care. Although PCD often does not respond to immunotherapy-especially in cases positive for anti-Yo antibodiesthere have been reports of clinical stabilization or modest improvement with immunosuppressive agents such as corticosteroids, intravenous immunoglobulin (IVIG), and plasmapheresis [3,5].
In our case, the initiation of high-dose dexamethasone resulted in mild symptomatic improvement. Surgical removal of the tumour has been reported to lead to stabilization or improvement of neurological symptoms in select cases [3]. Unfortunately, our patient developed postoperative urinary sepsis following debulking surgery and ultimately succumbed to this complication despite aggressive medical management. This case highlights the importance of considering PCD in the differential diagnosis of subacute cerebellar syndromes, especially when routine neuroimaging reveals no abnormalities, and vascular aetiologies have been excluded. A high index of suspicion, combined with targeted serological and oncological investigations, is essential for the early diagnosis and timely intervention in these rare but clinically significant syndromes.


=== INTRODUCTION ===

Paraneoplastic cerebellar degeneration (PCD) is a rare form of paraneoplastic neurological syndromes (PNS) that is rarely encountered in clinical practice. It affects less than 1% of patients with cancer and is most associated with cancers of the ovary, uterus, breast, small-cell lung carcinoma, and Hodgkin's lymphoma.
We present a case of a patient with an underlying ovarian tumour who experienced progressively worsening cerebellar dysfunction due to PCD. The initial presentation closely resembled an acute posterior circulation stroke, emphasizing the diagnostic challenges and the necessity of considering paraneoplastic causes in such clinical situations

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents an original clinical case with detailed patient presentation, investigations, and management, which constitutes primary observational research. It does not follow a systematic review methodology and contains no search strategy or synthesis of existing studies.  
**CONTEXT:**  
- Abstract: “Case presentation: We report the case of a 64‑year‑old woman….”  
- Discussion: “We present the case of a 64‑year‑old female who exhibited acute‑onset cerebellar symptoms….”  
- Introduction: “We present a case of a patient with an underlying ovarian tumour….”

**RESULT:** PASS  
**EXPLANATION:** The manuscript reports an original observation—a single patient case—providing new clinical data and analysis, which constitutes original research. It does not follow systematic review methodology.  
**CONTEXT:** “Case presentation: We report the case of a 64‑year‑old woman who experienced rapidly progressive cerebellar dysfunction… Subsequent investigations revealed a malignant ovarian tumour…” (Abstract) and “We present the case of a 64‑year‑old female who exhibited acute‑onset cerebellar symptoms… identified an underlying ovarian carcinoma.” (Discussion)

=== DISCUSSION ===

Paraneoplastic neurological syndromes (PNS) are rare, immune-mediated disorders that reflect the remote effects of systemic malignancies. These syndromes are usually associated with onconeural antibodies that target intracellular antigens shared by tumour cells and neurons. These antibodies can serve as diagnostic biomarkers and assist in identifying and localizing the underlying malignancy, thereby facilitating early and targeted therapeutic interventions [1,2].
One uncommon subtype of PNS is paraneoplastic cerebellar degeneration (PCD), which is characterized by subacute cerebellar dysfunction due to autoimmunemediated destruction of Purkinje cells. Clinically, PCD manifests as vertigo, nystagmus, diplopia, dysarthria, limb and truncal ataxia, and progressive gait instability. The condition often progresses rapidly, leading to significant neurological disability. Unfortunately, the prognosis is poor, as cerebellar damage is frequently irreversible, even after effective oncological treatment [1,3].
PCD has been associated with various onconeural antibodies, including anti-Yo, anti-Ri, anti-Tr, anti-Zic4, and anti-glutamic acid decarboxylase (GAD). Among these, anti-Yo antibodies are most frequently linked to gynaecologic and breast malignancies [3,4]. While the presence of onconeural antibodies supports the diagnosis of PNS, their absence does not rule it out, as seronegative cases are not uncommon [2,5]. This highlights the importance of a comprehensive clinical evaluation alongside ancillary testing.
We present the case of a 64-year-old female who exhibited acute-onset cerebellar symptoms without any brainstem pathology on neuroimaging. Further investigations, including spinal MRI, cerebrospinal fluid (CSF) analysis, a paraneoplastic antibody panel, tumour markers, and contrast-enhanced CT of the thorax, abdomen, and pelvis, identified an underlying ovarian carcinoma. The patient tested positive for anti-Yo antibodies, confirming a diagnosis of PCD. Notably, her neurological symptoms appeared before the diagnosis of malignancy. This temporal relationship aligns with existing literature, which indicates that PNS, particularly PCD, may precede the clinical detection of cancer by several months to years. In approximately 15% of reported cases, cerebellar symptoms emerged up to three years before tumour diagnosis [2,3]. Importantly, our patient showed no clinical signs or symptoms indicative of ovarian pathology at presentation, reinforcing the idea that PCD can be the initial and sole manifestation of an occult neoplasm.
Diagnostic evaluation of suspected PCD should be guided by clinical presentation and serological findings. Neuroimaging may reveal cerebellar atrophy in advanced cases, although early imaging can appear normal [1,3]. CSF analysis often shows mild lymphocytic pleocytosis, elevated protein levels, and unpaired oligoclonal bands, while cytological examination typically does not identify malignant cells [1,5]. Elevated tumour markers can provide additional diagnostic insights, particularly in cases involving gynaecologic or gastrointestinal malignancies.
Management of PCD necessitates prompt recognition and treatment of the underlying malignancy, which is critical for patient care. Although PCD often does not respond to immunotherapy-especially in cases positive for anti-Yo antibodiesthere have been reports of clinical stabilization or modest improvement with immunosuppressive agents such as corticosteroids, intravenous immunoglobulin (IVIG), and plasmapheresis [3,5].
In our case, the initiation of high-dose dexamethasone resulted in mild symptomatic improvement. Surgical removal of the tumour has been reported to lead to stabilization or improvement of neurological symptoms in select cases [3]. Unfortunately, our patient developed postoperative urinary sepsis following debulking surgery and ultimately succumbed to this complication despite aggressive medical management. This case highlights the importance of considering PCD in the differential diagnosis of subacute cerebellar syndromes, especially when routine neuroimaging reveals no abnormalities, and vascular aetiologies have been excluded. A high index of suspicion, combined with targeted serological and oncological investigations, is essential for the early diagnosis and timely intervention in these rare but clinically significant syndromes.


=== INTRODUCTION ===

Paraneoplastic cerebellar degeneration (PCD) is a rare form of paraneoplastic neurological syndromes (PNS) that is rarely encountered in clinical practice. It affects less than 1% of patients with cancer and is most associated with cancers of the ovary, uterus, breast, small-cell lung carcinoma, and Hodgkin's lymphoma.
We present a case of a patient with an underlying ovarian tumour who experienced progressively worsening cerebellar dysfunction due to PCD. The initial presentation closely resembled an acute posterior circulation stroke, emphasizing the diagnostic challenges and the necessity of considering paraneoplastic causes in such clinical situations


=== ABSTRACT ===
Background: Neurological manifestations of cancer can have multiple causes. Among these, paraneoplastic syndromes are disorders that arise from immune system responses, either as a direct consequence of the cancer itself or because of its treatment. While rare, paraneoplastic neurological syndromes can occur in association with ovarian tumours. Paraneoplastic cerebellar degeneration (PCD) often linked to the presence of onconeural antibodies may occur alongside ovarian neoplasms. Case presentation: We report the case of a 64-year-old woman who experienced rapidly progressive cerebellar dysfunction over a five-day period. Subsequent investigations revealed a malignant ovarian tumour, and her worsening neurological condition was attributed to a paraneoplastic neurological syndrome. Conclusion: This case underscores the importance of considering paraneoplastic cerebellar degeneration in the differential diagnosis of acute cerebellar syndromes, especially when initial neuroimaging rules out acute stroke. Notably, neurological symptoms often precede the diagnosis of an underlying malignancy. Therefore, the early recognition of a paraneoplastic syndrome should prompt an urgent and thorough search for an occult tumour


=== KEYWORDS ===
Paraneoplastic cerebellar degeneration||ovarian cancer||onconeuronal antibodies|| Stroke mimic


=== TITLE ===
Paraneoplastic cerebellar degeneration masquerading as acute stroke in a patient with ovarian tumour

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

Objective  
Confirm whether the manuscript includes new data or qualifies as a clinical research protocol, based on content required by medRxiv.

Instructions 
1. Analyze the manuscript for the presence of either:
	- New data from experiments, observations, clinical trials, or simulations, even if based on previously published sources (e.g., meta-analyses with new synthesis).
	- For a medRxiv submission, check whether the submission is a clinical research protocol, defined as a structured plan for a clinical study.
2. Treat meta-analyses and data syntheses from prior studies as valid forms of ""data"" for this check, if they involve original re-analysis or synthesis.
3. To confirm presence of new data:
	- Review tables, figures, or data presentations that are not directly reused from prior published work.
	- Look for explicit mentions of new findings or original analysis.
4. To confirm a clinical research protocol:
	- Check for structured information on study objectives, design, methods, eligibility criteria, or trial phases.
	- Trial registration numbers or ethics review statements are useful indicators.
5. Mark the manuscript as FAIL if neither new data nor clinical protocol indicators are found.
	- If the content is ambiguous or borderline, mark as FAIL with a clear explanation so that a human screener can review the context.

Output
1. Provide a PASS or FAIL result
	- Has New Data Or Is A Clinical Research Protocol: PASS
	- Does Not Have New Data Or A Clinical Research Protocol: FAIL
2. Provide a Detailed Explanation to evidence the PASS or FAIL result
3. Provide the context From the Paper to evidence the PASS or FAIL result

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

**SPECIFIC CHECK:**
Objective  
Confirm whether the manuscript includes new data or qualifies as a clinical research protocol, based on content required by medRxiv.

Instructions 
1. Analyze the manuscript for the presence of either:
	- New data from experiments, observations, clinical trials, or simulations, even if based on previously published sources (e.g., meta-analyses with new synthesis).
	- For a medRxiv submission, check whether the submission is a clinical research protocol, defined as a structured plan for a clinical study.
2. Treat meta-analyses and data syntheses from prior studies as valid forms of ""data"" for this check, if they involve original re-analysis or synthesis.
3. To confirm presence of new data:
	- Review tables, figures, or data presentations that are not directly reused from prior published work.
	- Look for explicit mentions of new findings or original analysis.
4. To confirm a clinical research protocol:
	- Check for structured information on study objectives, design, methods, eligibility criteria, or trial phases.
	- Trial registration numbers or ethics review statements are useful indicators.
5. Mark the manuscript as FAIL if neither new data nor clinical protocol indicators are found.
	- If the content is ambiguous or borderline, mark as FAIL with a clear explanation so that a human screener can review the context.

Output
1. Provide a PASS or FAIL result
	- Has New Data Or Is A Clinical Research Protocol: PASS
	- Does Not Have New Data Or A Clinical Research Protocol: FAIL
2. Provide a Detailed Explanation to evidence the PASS or FAIL result
3. Provide the context From the Paper to evidence the PASS or FAIL result



**DOCUMENT CONTENT TO ANALYZE:**

=== DISCUSSION ===

Paraneoplastic neurological syndromes (PNS) are rare, immune-mediated disorders that reflect the remote effects of systemic malignancies. These syndromes are usually associated with onconeural antibodies that target intracellular antigens shared by tumour cells and neurons. These antibodies can serve as diagnostic biomarkers and assist in identifying and localizing the underlying malignancy, thereby facilitating early and targeted therapeutic interventions [1,2].
One uncommon subtype of PNS is paraneoplastic cerebellar degeneration (PCD), which is characterized by subacute cerebellar dysfunction due to autoimmunemediated destruction of Purkinje cells. Clinically, PCD manifests as vertigo, nystagmus, diplopia, dysarthria, limb and truncal ataxia, and progressive gait instability. The condition often progresses rapidly, leading to significant neurological disability. Unfortunately, the prognosis is poor, as cerebellar damage is frequently irreversible, even after effective oncological treatment [1,3].
PCD has been associated with various onconeural antibodies, including anti-Yo, anti-Ri, anti-Tr, anti-Zic4, and anti-glutamic acid decarboxylase (GAD). Among these, anti-Yo antibodies are most frequently linked to gynaecologic and breast malignancies [3,4]. While the presence of onconeural antibodies supports the diagnosis of PNS, their absence does not rule it out, as seronegative cases are not uncommon [2,5]. This highlights the importance of a comprehensive clinical evaluation alongside ancillary testing.
We present the case of a 64-year-old female who exhibited acute-onset cerebellar symptoms without any brainstem pathology on neuroimaging. Further investigations, including spinal MRI, cerebrospinal fluid (CSF) analysis, a paraneoplastic antibody panel, tumour markers, and contrast-enhanced CT of the thorax, abdomen, and pelvis, identified an underlying ovarian carcinoma. The patient tested positive for anti-Yo antibodies, confirming a diagnosis of PCD. Notably, her neurological symptoms appeared before the diagnosis of malignancy. This temporal relationship aligns with existing literature, which indicates that PNS, particularly PCD, may precede the clinical detection of cancer by several months to years. In approximately 15% of reported cases, cerebellar symptoms emerged up to three years before tumour diagnosis [2,3]. Importantly, our patient showed no clinical signs or symptoms indicative of ovarian pathology at presentation, reinforcing the idea that PCD can be the initial and sole manifestation of an occult neoplasm.
Diagnostic evaluation of suspected PCD should be guided by clinical presentation and serological findings. Neuroimaging may reveal cerebellar atrophy in advanced cases, although early imaging can appear normal [1,3]. CSF analysis often shows mild lymphocytic pleocytosis, elevated protein levels, and unpaired oligoclonal bands, while cytological examination typically does not identify malignant cells [1,5]. Elevated tumour markers can provide additional diagnostic insights, particularly in cases involving gynaecologic or gastrointestinal malignancies.
Management of PCD necessitates prompt recognition and treatment of the underlying malignancy, which is critical for patient care. Although PCD often does not respond to immunotherapy-especially in cases positive for anti-Yo antibodiesthere have been reports of clinical stabilization or modest improvement with immunosuppressive agents such as corticosteroids, intravenous immunoglobulin (IVIG), and plasmapheresis [3,5].
In our case, the initiation of high-dose dexamethasone resulted in mild symptomatic improvement. Surgical removal of the tumour has been reported to lead to stabilization or improvement of neurological symptoms in select cases [3]. Unfortunately, our patient developed postoperative urinary sepsis following debulking surgery and ultimately succumbed to this complication despite aggressive medical management. This case highlights the importance of considering PCD in the differential diagnosis of subacute cerebellar syndromes, especially when routine neuroimaging reveals no abnormalities, and vascular aetiologies have been excluded. A high index of suspicion, combined with targeted serological and oncological investigations, is essential for the early diagnosis and timely intervention in these rare but clinically significant syndromes.


=== INTRODUCTION ===

Paraneoplastic cerebellar degeneration (PCD) is a rare form of paraneoplastic neurological syndromes (PNS) that is rarely encountered in clinical practice. It affects less than 1% of patients with cancer and is most associated with cancers of the ovary, uterus, breast, small-cell lung carcinoma, and Hodgkin's lymphoma.
We present a case of a patient with an underlying ovarian tumour who experienced progressively worsening cerebellar dysfunction due to PCD. The initial presentation closely resembled an acute posterior circulation stroke, emphasizing the diagnostic challenges and the necessity of considering paraneoplastic causes in such clinical situations


=== ABSTRACT ===
Background: Neurological manifestations of cancer can have multiple causes. Among these, paraneoplastic syndromes are disorders that arise from immune system responses, either as a direct consequence of the cancer itself or because of its treatment. While rare, paraneoplastic neurological syndromes can occur in association with ovarian tumours. Paraneoplastic cerebellar degeneration (PCD) often linked to the presence of onconeural antibodies may occur alongside ovarian neoplasms. Case presentation: We report the case of a 64-year-old woman who experienced rapidly progressive cerebellar dysfunction over a five-day period. Subsequent investigations revealed a malignant ovarian tumour, and her worsening neurological condition was attributed to a paraneoplastic neurological syndrome. Conclusion: This case underscores the importance of considering paraneoplastic cerebellar degeneration in the differential diagnosis of acute cerebellar syndromes, especially when initial neuroimaging rules out acute stroke. Notably, neurological symptoms often precede the diagnosis of an underlying malignancy. Therefore, the early recognition of a paraneoplastic syndrome should prompt an urgent and thorough search for an occult tumour


=== KEYWORDS ===
Paraneoplastic cerebellar degeneration||ovarian cancer||onconeuronal antibodies|| Stroke mimic


=== TITLE ===
Paraneoplastic cerebellar degeneration masquerading as acute stroke in a patient with ovarian tumour

**RESULT:** PASS  
**EXPLANATION:** The manuscript reports a single patient’s clinical presentation, diagnostic work‑up, treatment, and outcome, which constitutes new primary data. It is not a structured protocol but a case report containing original observations.  
**CONTEXT:** “We present the case of a 64‑year‑old female who exhibited acute‑onset cerebellar symptoms … further investigations, including spinal MRI, cerebrospinal fluid (CSF) analysis, a paraneoplastic antibody panel, tumour markers, and contrast‑enhanced CT of the thorax, abdomen, and pelvis, identified an underlying ovarian carcinoma. The patient tested positive for anti‑Yo antibodies, confirming a diagnosis of PCD… In our case, the initiation of high‑dose dexamethasone resulted in mild symptomatic improvement… Unfortunately, our patient developed postoperative urinary sepsis following debulking surgery and ultimately succumbed to this complication.”

**RESULT:** PASS  
**EXPLANATION:** The manuscript reports a previously unpublished clinical case with original patient data (diagnostic work‑up, treatment, and outcome), which constitutes new data. It does not present a structured clinical research protocol, but the presence of original case data satisfies the medRxiv requirement for new data.  
**CONTEXT:**  
- “We present the case of a 64‑year‑old female who exhibited acute‑onset cerebellar symptoms… identified an underlying ovarian carcinoma… The patient tested positive for anti‑Yo antibodies, confirming a diagnosis of PCD.”  
- “In our case, the initiation of high‑dose dexamethasone resulted in mild symptomatic improvement… The patient developed postoperative urinary sepsis following debulking surgery and ultimately succumbed to this complication.”

=== ABSTRACT ===
The three-dimensional (3D) printing has shown significant promise for creating objects from multiple materials, thanks to its ability to produce customized structures with distinct spatial arrangements. This approach holds transformative potential for the pharmaceutical field by allowing on-demand fabrication of personalized, multi-layered medications. In this study, a new stereolithographic (SLA) 3D printing technique was introduced, capable of producing multi-layered drug delivery systems-often referred to as polypills-with varied shapes and drug concentrations. Using this method, six different drugs-paracetamol, caffeine, naproxen, chloramphenicol, prednisolone, and aspirin-were printed in diverse shapes and material formats. Raman microscopy helped visualize how the drugs were distributed within the printed layers, revealing that although the layers were distinct, some drug migration occurred depending on whether the compounds were in an amorphous or crystalline state. The resulting polypills exhibited strong structural integrity and demonstrated varied drug release profiles in dissolution testing, influenced by their material compositions. This research marks a significant advancement in using SLA 3D printing for producing customizable multi-drug therapies, paving the way for a new generation of patient-specific theraphy


=== KEYWORDS ===
Polypharmacy||Treatment||3D printing||Analysis||API||Polymer||Disintegrants||Diabetes


=== TITLE ===
Polypill : A Revolutionizing Preventative Medicine


=== INTRODUCTION ===

The treatment of complex chronic diseases such as HIV-1, hypertension, tuberculosis, and type 2 diabetes often require polypharmacy-the concurrent use of five or more medications. While this approach improves clinical outcomes, it also poses challenges like poor patient adherence, increased risk of medication errors, and confusion due to complex dosing schedules.
To simplify treatment, fixed-dose combinations (FDCs) or polypills have been developed. These combine multiple active pharmaceutical ingredients into a single tablet, improving compliance and clinical efficacy. A notable example is Polycap™, which combines aspirin, ramipril, simvastatin, atenolol, and hydrochlorothiazide for cardiovascular disease prevention. However, traditional manufacturing methods such as powder compaction offer limited flexibility, making it difficult to adjust drug doses or change components in a fixed formulation. As a result, patients requiring dose modifications often revert to multiple separate medications, defeating the purpose of combination therapy.
In response, personalized medicine has driven the need for more adaptable manufacturing technologies. Three-dimensional printing (3DP) has emerged as a promising solution, offering unprecedented control over tablet design, drug release profiles, and dosage customization. It enables the creation of complex geometries and multi-compound tablets through layer-by-layer construction based on digital models.
One of 3DP's major advantages is its ability to physically separate multiple drugs within a single tablet, allowing for independent dose control and release kinetics. Past research has demonstrated multi-drug tablets using 3DP with spatial separation, such as layered tablets containing paracetamol and caffeine, and complex five-drug polypills with distinct release profiles. Among 3D Printing methods, fused deposition modeling (FDM) is most commonly used but limited by its need for drug-loaded filaments and a small number of usable materials. This restricts the number of drugs that can be included with clear spatial distinction.
Alternatively, stereolithography (SLA)-a technique that uses lasers to cure layers of liquid resin-offers several advantages: no heat (avoiding thermal degradation of drugs), higher precision, and faster printing. While SLA has shown success in printing drug-loaded tablets and hydrogels, its use for polypills has been limited due to the difficulty of printing separate layers from multiple resins.
This study addresses that challenge by introducing a custom-built SLA 3D printer capable of printing multi-layered polypills containing six model drugs-paracetamol, naproxen, caffeine, aspirin, prednisolone, and chloramphenicol.
These drugs represent various therapeutic classes and are often used in combination therapies. Raman microscopy was employed to confirm the spatial separation of each drug within the printed tablets. Additionally, the influence of tablet geometry (cylindrical vs. ring-shaped) and excipients on drug release was evaluated through dissolution studies.
The successful fabrication of these personalized, multi-drug tablets using SLA represents a significant technological breakthrough. It demonstrates the potential of 3D printing to revolutionize pharmaceutical_manufacturing, particularly in enabling customized therapies tailored to individual patient needs, minimizing pill burden, and enhancing treatment adherence.

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

Objective  
Confirm whether the manuscript qualifies as a research paper or a systematic review based on structural and methodological characteristics.

Instructions  
1. Evaluate the manuscript for the following:
 - A research paper presents original research findings based on experiments, observations, or analyses conducted by the authors.
 - A systematic review provides a structured review of prior research following a clear methodology (e.g., inclusion/exclusion criteria, data synthesis).
2. For systematic reviews, look for indicators such as:
 - Mention of search strategies, search terms, or databases used.
 - Use of established frameworks like PRISMA, PICO, or equivalent (optional but useful indicators).
3. Check for presence of standard sections:
 - Abstract, Introduction, Methods, Results, Discussion/Conclusion.
 - Accept variants such as Background, Materials and Methods, Findings, or Conclusions.
4. Look for language suggesting original research or systematic methodology, such as:
 - “we conducted experiments,” “our findings show,” “we systematically reviewed,” “studies were selected based on.”
5. Mark the manuscript as FAIL if it lacks structural and methodological indicators of original research or systematic review.
- Narrative summaries, essays, or theory pieces without structured methodology should be marked FAIL.
6.  If it IS a research paper or a systematic review, mark as PASS. If it is NOT a research paper or a systematic review, mark as FAIL.

Output Format
- PASS / FAIL
- Detailed Explanation
- Context From the Paper

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

**SPECIFIC CHECK:**
Objective  
Confirm whether the manuscript qualifies as a research paper or a systematic review based on structural and methodological characteristics.

Instructions  
1. Evaluate the manuscript for the following:
 - A research paper presents original research findings based on experiments, observations, or analyses conducted by the authors.
 - A systematic review provides a structured review of prior research following a clear methodology (e.g., inclusion/exclusion criteria, data synthesis).
2. For systematic reviews, look for indicators such as:
 - Mention of search strategies, search terms, or databases used.
 - Use of established frameworks like PRISMA, PICO, or equivalent (optional but useful indicators).
3. Check for presence of standard sections:
 - Abstract, Introduction, Methods, Results, Discussion/Conclusion.
 - Accept variants such as Background, Materials and Methods, Findings, or Conclusions.
4. Look for language suggesting original research or systematic methodology, such as:
 - “we conducted experiments,” “our findings show,” “we systematically reviewed,” “studies were selected based on.”
5. Mark the manuscript as FAIL if it lacks structural and methodological indicators of original research or systematic review.
- Narrative summaries, essays, or theory pieces without structured methodology should be marked FAIL.
6.  If it IS a research paper or a systematic review, mark as PASS. If it is NOT a research paper or a systematic review, mark as FAIL.

Output Format
- PASS / FAIL
- Detailed Explanation
- Context From the Paper

**DOCUMENT CONTENT TO ANALYZE:**

=== ABSTRACT ===
The three-dimensional (3D) printing has shown significant promise for creating objects from multiple materials, thanks to its ability to produce customized structures with distinct spatial arrangements. This approach holds transformative potential for the pharmaceutical field by allowing on-demand fabrication of personalized, multi-layered medications. In this study, a new stereolithographic (SLA) 3D printing technique was introduced, capable of producing multi-layered drug delivery systems-often referred to as polypills-with varied shapes and drug concentrations. Using this method, six different drugs-paracetamol, caffeine, naproxen, chloramphenicol, prednisolone, and aspirin-were printed in diverse shapes and material formats. Raman microscopy helped visualize how the drugs were distributed within the printed layers, revealing that although the layers were distinct, some drug migration occurred depending on whether the compounds were in an amorphous or crystalline state. The resulting polypills exhibited strong structural integrity and demonstrated varied drug release profiles in dissolution testing, influenced by their material compositions. This research marks a significant advancement in using SLA 3D printing for producing customizable multi-drug therapies, paving the way for a new generation of patient-specific theraphy


=== KEYWORDS ===
Polypharmacy||Treatment||3D printing||Analysis||API||Polymer||Disintegrants||Diabetes


=== TITLE ===
Polypill : A Revolutionizing Preventative Medicine


=== INTRODUCTION ===

The treatment of complex chronic diseases such as HIV-1, hypertension, tuberculosis, and type 2 diabetes often require polypharmacy-the concurrent use of five or more medications. While this approach improves clinical outcomes, it also poses challenges like poor patient adherence, increased risk of medication errors, and confusion due to complex dosing schedules.
To simplify treatment, fixed-dose combinations (FDCs) or polypills have been developed. These combine multiple active pharmaceutical ingredients into a single tablet, improving compliance and clinical efficacy. A notable example is Polycap™, which combines aspirin, ramipril, simvastatin, atenolol, and hydrochlorothiazide for cardiovascular disease prevention. However, traditional manufacturing methods such as powder compaction offer limited flexibility, making it difficult to adjust drug doses or change components in a fixed formulation. As a result, patients requiring dose modifications often revert to multiple separate medications, defeating the purpose of combination therapy.
In response, personalized medicine has driven the need for more adaptable manufacturing technologies. Three-dimensional printing (3DP) has emerged as a promising solution, offering unprecedented control over tablet design, drug release profiles, and dosage customization. It enables the creation of complex geometries and multi-compound tablets through layer-by-layer construction based on digital models.
One of 3DP's major advantages is its ability to physically separate multiple drugs within a single tablet, allowing for independent dose control and release kinetics. Past research has demonstrated multi-drug tablets using 3DP with spatial separation, such as layered tablets containing paracetamol and caffeine, and complex five-drug polypills with distinct release profiles. Among 3D Printing methods, fused deposition modeling (FDM) is most commonly used but limited by its need for drug-loaded filaments and a small number of usable materials. This restricts the number of drugs that can be included with clear spatial distinction.
Alternatively, stereolithography (SLA)-a technique that uses lasers to cure layers of liquid resin-offers several advantages: no heat (avoiding thermal degradation of drugs), higher precision, and faster printing. While SLA has shown success in printing drug-loaded tablets and hydrogels, its use for polypills has been limited due to the difficulty of printing separate layers from multiple resins.
This study addresses that challenge by introducing a custom-built SLA 3D printer capable of printing multi-layered polypills containing six model drugs-paracetamol, naproxen, caffeine, aspirin, prednisolone, and chloramphenicol.
These drugs represent various therapeutic classes and are often used in combination therapies. Raman microscopy was employed to confirm the spatial separation of each drug within the printed tablets. Additionally, the influence of tablet geometry (cylindrical vs. ring-shaped) and excipients on drug release was evaluated through dissolution studies.
The successful fabrication of these personalized, multi-drug tablets using SLA represents a significant technological breakthrough. It demonstrates the potential of 3D printing to revolutionize pharmaceutical_manufacturing, particularly in enabling customized therapies tailored to individual patient needs, minimizing pill burden, and enhancing treatment adherence.

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents original experimental work: a custom SLA 3D printer was built, six drugs were printed in distinct layers, Raman microscopy and dissolution testing were performed, and the results are reported. This structure—introduction, methods, results, discussion—matches a research paper, not a systematic review.  
**CONTEXT:**  
- Abstract: “Using this method, six different drugs… were printed… Raman microscopy helped visualize… The resulting polypills exhibited strong structural integrity and demonstrated varied drug release profiles in dissolution testing…”  
- Introduction: “This study addresses that challenge by introducing a custom-built SLA 3D printer… Raman microscopy was employed… dissolution studies.”  
- The manuscript describes specific experimental procedures and findings, indicating original research.

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents original experimental work: a new SLA 3D printing method, fabrication of six drug‑loaded polypills, characterization by Raman microscopy, and dissolution testing. It follows the typical structure of a research paper with Abstract, Introduction, Methods (implied in the description of printing, Raman, dissolution), Results, and Discussion/Conclusion.  
**CONTEXT:** “In this study, a new stereolithographic (SLA) 3D printing technique was introduced… Using this method, six different drugs… were printed… Raman microscopy helped visualize how the drugs were distributed… The resulting polypills exhibited strong structural integrity and demonstrated varied drug release profiles in dissolution testing… This research marks a significant advancement….”

=== INTRODUCTION ===

The treatment of complex chronic diseases such as HIV-1, hypertension, tuberculosis, and type 2 diabetes often require polypharmacy-the concurrent use of five or more medications. While this approach improves clinical outcomes, it also poses challenges like poor patient adherence, increased risk of medication errors, and confusion due to complex dosing schedules.
To simplify treatment, fixed-dose combinations (FDCs) or polypills have been developed. These combine multiple active pharmaceutical ingredients into a single tablet, improving compliance and clinical efficacy. A notable example is Polycap™, which combines aspirin, ramipril, simvastatin, atenolol, and hydrochlorothiazide for cardiovascular disease prevention. However, traditional manufacturing methods such as powder compaction offer limited flexibility, making it difficult to adjust drug doses or change components in a fixed formulation. As a result, patients requiring dose modifications often revert to multiple separate medications, defeating the purpose of combination therapy.
In response, personalized medicine has driven the need for more adaptable manufacturing technologies. Three-dimensional printing (3DP) has emerged as a promising solution, offering unprecedented control over tablet design, drug release profiles, and dosage customization. It enables the creation of complex geometries and multi-compound tablets through layer-by-layer construction based on digital models.
One of 3DP's major advantages is its ability to physically separate multiple drugs within a single tablet, allowing for independent dose control and release kinetics. Past research has demonstrated multi-drug tablets using 3DP with spatial separation, such as layered tablets containing paracetamol and caffeine, and complex five-drug polypills with distinct release profiles. Among 3D Printing methods, fused deposition modeling (FDM) is most commonly used but limited by its need for drug-loaded filaments and a small number of usable materials. This restricts the number of drugs that can be included with clear spatial distinction.
Alternatively, stereolithography (SLA)-a technique that uses lasers to cure layers of liquid resin-offers several advantages: no heat (avoiding thermal degradation of drugs), higher precision, and faster printing. While SLA has shown success in printing drug-loaded tablets and hydrogels, its use for polypills has been limited due to the difficulty of printing separate layers from multiple resins.
This study addresses that challenge by introducing a custom-built SLA 3D printer capable of printing multi-layered polypills containing six model drugs-paracetamol, naproxen, caffeine, aspirin, prednisolone, and chloramphenicol.
These drugs represent various therapeutic classes and are often used in combination therapies. Raman microscopy was employed to confirm the spatial separation of each drug within the printed tablets. Additionally, the influence of tablet geometry (cylindrical vs. ring-shaped) and excipients on drug release was evaluated through dissolution studies.
The successful fabrication of these personalized, multi-drug tablets using SLA represents a significant technological breakthrough. It demonstrates the potential of 3D printing to revolutionize pharmaceutical_manufacturing, particularly in enabling customized therapies tailored to individual patient needs, minimizing pill burden, and enhancing treatment adherence.


=== ABSTRACT ===
The three-dimensional (3D) printing has shown significant promise for creating objects from multiple materials, thanks to its ability to produce customized structures with distinct spatial arrangements. This approach holds transformative potential for the pharmaceutical field by allowing on-demand fabrication of personalized, multi-layered medications. In this study, a new stereolithographic (SLA) 3D printing technique was introduced, capable of producing multi-layered drug delivery systems-often referred to as polypills-with varied shapes and drug concentrations. Using this method, six different drugs-paracetamol, caffeine, naproxen, chloramphenicol, prednisolone, and aspirin-were printed in diverse shapes and material formats. Raman microscopy helped visualize how the drugs were distributed within the printed layers, revealing that although the layers were distinct, some drug migration occurred depending on whether the compounds were in an amorphous or crystalline state. The resulting polypills exhibited strong structural integrity and demonstrated varied drug release profiles in dissolution testing, influenced by their material compositions. This research marks a significant advancement in using SLA 3D printing for producing customizable multi-drug therapies, paving the way for a new generation of patient-specific theraphy


=== KEYWORDS ===
Polypharmacy||Treatment||3D printing||Analysis||API||Polymer||Disintegrants||Diabetes


=== TITLE ===
Polypill : A Revolutionizing Preventative Medicine

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

Objective  
Confirm whether the manuscript includes new data or qualifies as a clinical research protocol, based on content required by medRxiv.

Instructions 
1. Analyze the manuscript for the presence of either:
	- New data from experiments, observations, clinical trials, or simulations, even if based on previously published sources (e.g., meta-analyses with new synthesis).
	- For a medRxiv submission, check whether the submission is a clinical research protocol, defined as a structured plan for a clinical study.
2. Treat meta-analyses and data syntheses from prior studies as valid forms of ""data"" for this check, if they involve original re-analysis or synthesis.
3. To confirm presence of new data:
	- Review tables, figures, or data presentations that are not directly reused from prior published work.
	- Look for explicit mentions of new findings or original analysis.
4. To confirm a clinical research protocol:
	- Check for structured information on study objectives, design, methods, eligibility criteria, or trial phases.
	- Trial registration numbers or ethics review statements are useful indicators.
5. Mark the manuscript as FAIL if neither new data nor clinical protocol indicators are found.
	- If the content is ambiguous or borderline, mark as FAIL with a clear explanation so that a human screener can review the context.

Output
1. Provide a PASS or FAIL result
	- Has New Data Or Is A Clinical Research Protocol: PASS
	- Does Not Have New Data Or A Clinical Research Protocol: FAIL
2. Provide a Detailed Explanation to evidence the PASS or FAIL result
3. Provide the context From the Paper to evidence the PASS or FAIL result

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

**SPECIFIC CHECK:**
Objective  
Confirm whether the manuscript includes new data or qualifies as a clinical research protocol, based on content required by medRxiv.

Instructions 
1. Analyze the manuscript for the presence of either:
	- New data from experiments, observations, clinical trials, or simulations, even if based on previously published sources (e.g., meta-analyses with new synthesis).
	- For a medRxiv submission, check whether the submission is a clinical research protocol, defined as a structured plan for a clinical study.
2. Treat meta-analyses and data syntheses from prior studies as valid forms of ""data"" for this check, if they involve original re-analysis or synthesis.
3. To confirm presence of new data:
	- Review tables, figures, or data presentations that are not directly reused from prior published work.
	- Look for explicit mentions of new findings or original analysis.
4. To confirm a clinical research protocol:
	- Check for structured information on study objectives, design, methods, eligibility criteria, or trial phases.
	- Trial registration numbers or ethics review statements are useful indicators.
5. Mark the manuscript as FAIL if neither new data nor clinical protocol indicators are found.
	- If the content is ambiguous or borderline, mark as FAIL with a clear explanation so that a human screener can review the context.

Output
1. Provide a PASS or FAIL result
	- Has New Data Or Is A Clinical Research Protocol: PASS
	- Does Not Have New Data Or A Clinical Research Protocol: FAIL
2. Provide a Detailed Explanation to evidence the PASS or FAIL result
3. Provide the context From the Paper to evidence the PASS or FAIL result



**DOCUMENT CONTENT TO ANALYZE:**

=== INTRODUCTION ===

The treatment of complex chronic diseases such as HIV-1, hypertension, tuberculosis, and type 2 diabetes often require polypharmacy-the concurrent use of five or more medications. While this approach improves clinical outcomes, it also poses challenges like poor patient adherence, increased risk of medication errors, and confusion due to complex dosing schedules.
To simplify treatment, fixed-dose combinations (FDCs) or polypills have been developed. These combine multiple active pharmaceutical ingredients into a single tablet, improving compliance and clinical efficacy. A notable example is Polycap™, which combines aspirin, ramipril, simvastatin, atenolol, and hydrochlorothiazide for cardiovascular disease prevention. However, traditional manufacturing methods such as powder compaction offer limited flexibility, making it difficult to adjust drug doses or change components in a fixed formulation. As a result, patients requiring dose modifications often revert to multiple separate medications, defeating the purpose of combination therapy.
In response, personalized medicine has driven the need for more adaptable manufacturing technologies. Three-dimensional printing (3DP) has emerged as a promising solution, offering unprecedented control over tablet design, drug release profiles, and dosage customization. It enables the creation of complex geometries and multi-compound tablets through layer-by-layer construction based on digital models.
One of 3DP's major advantages is its ability to physically separate multiple drugs within a single tablet, allowing for independent dose control and release kinetics. Past research has demonstrated multi-drug tablets using 3DP with spatial separation, such as layered tablets containing paracetamol and caffeine, and complex five-drug polypills with distinct release profiles. Among 3D Printing methods, fused deposition modeling (FDM) is most commonly used but limited by its need for drug-loaded filaments and a small number of usable materials. This restricts the number of drugs that can be included with clear spatial distinction.
Alternatively, stereolithography (SLA)-a technique that uses lasers to cure layers of liquid resin-offers several advantages: no heat (avoiding thermal degradation of drugs), higher precision, and faster printing. While SLA has shown success in printing drug-loaded tablets and hydrogels, its use for polypills has been limited due to the difficulty of printing separate layers from multiple resins.
This study addresses that challenge by introducing a custom-built SLA 3D printer capable of printing multi-layered polypills containing six model drugs-paracetamol, naproxen, caffeine, aspirin, prednisolone, and chloramphenicol.
These drugs represent various therapeutic classes and are often used in combination therapies. Raman microscopy was employed to confirm the spatial separation of each drug within the printed tablets. Additionally, the influence of tablet geometry (cylindrical vs. ring-shaped) and excipients on drug release was evaluated through dissolution studies.
The successful fabrication of these personalized, multi-drug tablets using SLA represents a significant technological breakthrough. It demonstrates the potential of 3D printing to revolutionize pharmaceutical_manufacturing, particularly in enabling customized therapies tailored to individual patient needs, minimizing pill burden, and enhancing treatment adherence.


=== ABSTRACT ===
The three-dimensional (3D) printing has shown significant promise for creating objects from multiple materials, thanks to its ability to produce customized structures with distinct spatial arrangements. This approach holds transformative potential for the pharmaceutical field by allowing on-demand fabrication of personalized, multi-layered medications. In this study, a new stereolithographic (SLA) 3D printing technique was introduced, capable of producing multi-layered drug delivery systems-often referred to as polypills-with varied shapes and drug concentrations. Using this method, six different drugs-paracetamol, caffeine, naproxen, chloramphenicol, prednisolone, and aspirin-were printed in diverse shapes and material formats. Raman microscopy helped visualize how the drugs were distributed within the printed layers, revealing that although the layers were distinct, some drug migration occurred depending on whether the compounds were in an amorphous or crystalline state. The resulting polypills exhibited strong structural integrity and demonstrated varied drug release profiles in dissolution testing, influenced by their material compositions. This research marks a significant advancement in using SLA 3D printing for producing customizable multi-drug therapies, paving the way for a new generation of patient-specific theraphy


=== KEYWORDS ===
Polypharmacy||Treatment||3D printing||Analysis||API||Polymer||Disintegrants||Diabetes


=== TITLE ===
Polypill : A Revolutionizing Preventative Medicine

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents original experimental data generated by the authors: a custom SLA 3D printer was used to fabricate multi‑layered polypills containing six drugs, Raman microscopy was performed to assess spatial drug distribution, and dissolution studies were conducted to evaluate release profiles. These constitute new data from experiments, satisfying medRxiv’s requirement.  
**CONTEXT:** “Using this method, six different drugs‑paracetamol, caffeine, naproxen, chloramphenicol, prednisolone, and aspirin‑were printed in diverse shapes and material formats. Raman microscopy helped visualize how the drugs were distributed within the printed layers, revealing that although the layers were distinct, some drug migration occurred depending on whether the compounds were in an amorphous or crystalline state. The resulting polypills exhibited strong structural integrity and demonstrated varied drug release profiles in dissolution testing, influenced by their material compositions.”

**RESULT:** PASS  
**EXPLANATION:** The manuscript reports original experimental work: a custom SLA 3D printer was used to fabricate multi‑layered polypills containing six distinct drugs, followed by Raman microscopy to assess spatial drug distribution and dissolution testing to evaluate release profiles. These constitute new data generated by the authors.  
**CONTEXT:** “Using this method, six different drugs‑paracetamol, caffeine, naproxen, chloramphenicol, prednisolone, and aspirin‑were printed in diverse shapes and material formats. Raman microscopy helped visualize how the drugs were distributed within the printed layers, revealing that although the layers were distinct, some drug migration occurred… The resulting polypills exhibited strong structural integrity and demonstrated varied drug release profiles in dissolution testing, influenced by their material compositions.”

=== ABSTRACT ===
Artificial intelligence (AI) in healthcare holds transformative potential but risks exacerbating existing health disparities if inclusivity is not explicitly accounted for. This study addresses the disconnected discussions on inclusive medical AI by developing a comprehensive framework, PREFER-IT. This framework is based on the outcomes of a five-day transdisciplinary cocreation workshop that involved 37 experts from diverse backgrounds, including healthcare, ethics, law, social sciences, AI, and patient advocacy. For this workshop, we used design thinking and participatory methodologies to develop a framework for realising inclusive medical AI. We identified three key challenges for realising inclusive medical AI: integrating the lived experiences and stakeholder voices across the AI lifecycle, designing data collection practices that promote fairness and prevent inequalities, and fostering regulatory frameworks to uphold human rights and promote inclusivity. The analysis of participants' perspectives informed the development of eight key thematic clusters of PREFER-IT: Participatory and codesign approaches (P), Representative and diverse data (R), Education and digital literacy (E), Fairness (F), Ethical and legal accountability (E), Real-world validation and feedback (R), Inclusive communication (I), and Technical interoperability (T). These elements were mapped across structural layers of AI (humans, data, system, process, and governance) and the AI lifecycle to guide inclusive design, development, validation, implementation, monitoring, and governance. This framework fosters stakeholder engagement and systemic change, positioning inclusion as a guiding principle in practice. PREFER-IT offers a practical and conceptual contribution for how to include ethical, legal and societal aspects when aiming to foster responsible and inclusive AI in healthcare.


=== KEYWORDS ===
PPF|| SSL|| WB|| SdK|| CL|| LB|| MP||Data Curation: PPF|| SSL|| WB|| LB||Investigation: PPF|| SSL|| WB|| JG|| LB|| MP||Methodology: PPF|| SSL|| WB|| JG|| LB|| MP||Supervision: SdK|| CL|| MP||Visualisation: PPF||Writing -Original Draft Preparation: PPF||Writing -Review & Editing: all the authors


=== TITLE ===
PREFER-IT: A transdisciplinary co-created framework to realise inclusive medical AI


=== SUPPLEMENTARY ===

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


=== DISCUSSION ===

This paper presents PREFER-IT, a set of co-created foundational requirements developed through a transdisciplinary workshop to provide an operational framework for realising inclusivity throughout the AI lifecycle and governance in healthcare. By engaging a diverse group of stakeholders in a structured co-creation process, we translated lived experiences and expert insights into actionable requirements for inclusive medical AI.
The workshop combined a preparatory PPI meeting and survey, a consensus-building exercise, and design thinking methods, including patient personas and journey mapping with the active involvement of patient experts. This structured process progressively moved from the broad challenge of inclusive medical AI to a set of concrete, experience-informed solutions and requirements, eventually culminating in the development of the PREFER-IT framework.
While existing frameworks often address fairness, trustworthiness or ethics in medical AI [2,9,18,25,[43][44][45][46], they generally remain conceptual or focus on technical dimensions. A unified, actionable framework for AI inclusivity, particularly within healthcare, remains absent, at least to our knowledge. PREFER-IT is a first step to respond to this gap by offering both a conceptual and applied foundation to guide the inclusive design, development, validation, implementation, monitoring, and governance of medical AI.
The co-creation process involved a small number of participants, providing an in-depth and explorative knowledge and exchange that generated valuable insights. The diversity of expertise and richness of perspectives provide a robust qualitative foundation for further empirical investigation. The co-created solutions, grounded in patient voices and lived experiences, particularly emphasised the human and governance dimensions of AI, reflecting the workshop's interdisciplinary composition across social sciences, ethics, and health policy.
However, the framework remains adaptable to technical domains and can guide integration at the data, process, and system levels of medical AI. While the discussions were primarily situated within a European context, this framing offers a strong basis for extending and validating the framework in other settings. A possible next step could be to test and adapt the framework across diverse contexts to capture a broader range of epistemic perspectives and societal concerns, ensuring its global relevance and applicability.
The workshop also revealed the importance of authentic participation. While patient involvement enriched the framework, it also highlighted risks of tokenism. Similar tensions have been observed in participatory AI design more broadly, where researchers and practitioners often struggle to reconcile participatory ambitions with practical constraints such as time, resources, and institutional norms [47]. To navigate these challenges, teams frequently rely on proxies (like stand-ins for stakeholders) that may inadvertently reinforce existing power asymmetries rather than dismantle them [47]. This underscores the need for co-creation to extend beyond isolated workshops toward sustained, reflexive engagement mechanisms that distribute decision-making authority and epistemic legitimacy. Without such safeguards, inclusion risks being symbolic rather than transformative.
Participants also identified several persistent gaps for realising inclusive medical AI. Rather than obstacles, these can be reframed as priority areas where PREFER-IT should be actively applied or advocated. For instance, the call for support tools to facilitate guideline adoption and compliance highlights the need for stronger public engagement in shaping and implementing AI regulation, as illustrated by the Algorithm Register of the Dutch government [48]. At the policy level, incorporating PREFER-IT principles into funding criteria, regulatory assessments, and institutional governance could help move inclusion from aspiration to routine practice. This approach would shape AI medical inclusivity in what is developed, how it is developed, who develops it, and how it is evaluated and refined over time.
Likewise, the demand for educational and capacity-building initiatives, such as patient training programs or education tools, aligns with ongoing policy efforts, such as those outlined in the AI Act [49], to support sustained implementation of PREFER-IT. Capacity-building should also target AI developers and healthcare professionals to bridge disciplinary gaps. As AI researchers often lack medical expertise, collaboration with clinicians and patients is crucial for understanding the clinical context and translating patient needs into technical design requirements. The more systemic concerns, including implementation complexity, safeguarding ethics, and balancing diverse values, highlight that PREFER-IT should not be approached as a simple checklist. Instead, it needs to be an adaptable infrastructure spanning the entire AI lifecycle and its various structural layers. The core challenge lies not in the absence of ethical discourse but in translating principles into actionable, context-sensitive mechanisms that can keep pace with technological innovation. Viewing inclusivity as a behavioural process that requires capability, opportunity, and motivation could strengthen the framework's practical applicability and promote organisational cultures that sustain inclusive AI development. [50] Looking ahead, the next steps involve translating PREFER-IT from a co-created framework into a living infrastructure for inclusive medical AI. This will require ongoing engagement with diverse stakeholders, including patients with intersectional vulnerabilities, regulators, and insurers. It also involves iterative testing in real-world projects, systematic evaluation across healthcare contexts, and continuous refinement based on user and patient feedback. Through this operationalisation, PREFER-IT aims to advance AI in healthcare that is not only effective but also equitable, ethical, legal, and socially responsible [51]. The co-creation process highlighted that realising inclusivity in medical AI requires more than just design features, depending on systemic enablers across multiple levels. In line with Wang and Blok's multi-level framework [25], PREFER-IT can be operationalised at the micro-level (individual AI issues), meso-level (organisational and systemic issues), and macro-level (philosophical issues). For example, policymakers can mandate transparency and equity audits; developers can integrate inclusion-by-design and participatory testing; researchers and clinicians can promote representative study designs; and patients and communities can codefine research priorities and evaluation metrics. The PREFER-IT framework could provide a basis for coordinating these actions across roles and governance levels. Finally, our research suggests a broader reframing: from an innovation-first logic to an inclusive-by-design paradigm. This shift positions inclusivity as a constitutive element of medical AI, embedded not only in outcomes but also in processes, governance structures, and power relations. By doing so, PREFER-IT helps address epistemic hierarchies in knowledge generation, challenging the dominance of quantitative and technical evidence and elevating the value of qualitative insights, experiential knowledge, and patient voices in shaping AI development. Through the development of PREFER-IT, we offer both a conceptual contribution and a practical resource to support stakeholders in realising inclusivity throughout the AI lifecycle in healthcare. The framework builds on, but also extends beyond, existing frameworks by positioning inclusion not as a peripheral ethical concern but as a core design principle, rooted in equity, stakeholder engagement, and governance accountability. Crucially, this process revealed that advancing inclusion in medical AI is not merely a matter of technological or design choices. It requires reconfiguring institutional logics, incentive structures, and policy environments. The PREFER-IT framework captures these multi-level dynamics and provides a framework for coordinated action. By explicitly incorporating the roles of patients and communities, it helps bridge the gap between AI development and implementation and the lived realities of those most affected by it. Ultimately, this process challenges dominant epistemic practices and paradigms in medical innovation, which often prioritise efficiency and technical advancement over equity and participation. Co-creation, as adopted here, is not merely a methodological technique but a stance that recognises the value of inclusion. It shifts the focus from innovation for to innovation with communities. 14,8% 18,5% 50,0% 20,0% 30,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 50,0% 25,0% 25,0% 1 Understanding the effects of bias on AI outputs, such as unfair recommendations. 27 15 3 9 0 10 2 3 5 0 4 4 0 0 0 5 5 0 0 0 8 4 0 4 0 55,6% 11,1% 33,3% 20,0% 30,0% 50,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 50,0% 0,0% 50,0% 2 Including diverse stakeholder voices throughout the AI lifecycle. 26 21 1 4 1 9 7 0 2 1 4 3 1 0 0 5 5 0 0 0 8 6 0 2 0 80,8% 3,8% 15,4% 77,8% 0,0% 22,2% 75,0% 25,0% 0,0% 100,0% 0,0% 0,0% 75,0% 0,0% 25,0% 3 Including the lived experiences of diverse groups in AI design and implementation. 27 22 0 5 0 10 8 0 2 0 4 4 0 0 0 5 5 0 0 0 8 5 0 3 0 81,5% 0,0% 18,5% 80,0% 0,0% 20,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 62,5% 0,0% 37,5% 4 Developing regulatory frameworks that address inclusivity in medical AI. 26 15 4 7 1 9 4 3 2 1 4 1 1 2 0 5 5 0 0 0 8 5 0 3 0 57,7% 15,4% 26,9% 44,4% 33,3% 22,2% 25,0% 25,0% 50,0% 100,0% 0,0% 0,0% 62,5% 0,0% 37,5% 5 Creating regulatory frameworks interpretable for all stakeholders. 27 10 9 8 0 10 0 6 4 0 4 0 2 2 0 5 5 0 0 0 8 5 1 2 0 37,0% 33,3% 29,6% 0,0% 60,0% 40,0% 0,0% 50,0% 50,0% 100,0% 0,0% 0,0% 62,5% 12,5% 25,0% 6 Ensuring training datasets represent diverse groups. 27 18 3 6 0 10 5 2 3 0 4 2 1 1 0 5 5 0 0 0 8 6 0 2 0 66,7% 11,1% 22,2% 50,0% 20,0% 30,0% 50,0% 25,0% 25,0% 100,0% 0,0% 0,0% 75,0% 0,0% 25,0% Assessing whether current data collection practices exacerbate inequalities.
27 20 3 4 0 10 5 3 2 0 4 4 0 0 0 5 5 0 0 0 8 6 0 2 0 74,1%
11,1% 14,8% 50,0% 30,0% 20,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 75,0% 0,0% 25,0% Investigating the societal impact of AI systems on diverse groups. 27 17 5 5 0 10 4 4 2 0 4 4 0 0 0 5 5 0 0 0 8 4 1 3 63,0% 18,5% 18,5% 40,0% 40,0% 20,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 50,0% 12,5% 37,5% 9 Continuous evaluation of AI across diverse groups. 26 13 4 9 1 9 4 3 2 1 4 1 1 2 0 5 5 0 0 0 8 3 0 5 50,0% 15,4% 34,6% 44,4% 33,3% 22,2% 25,0% 25,0% 50,0% 100,0% 0,0% 0,0% 37,5% 0,0% 62,5% 10 Identifying how medical AI affects human rights of diverse groups. 27 10 9 8 0 10 0 7 3 0 4 1 1 2 0 5 5 0 0 0 8 4 1 3 37,0% 33,3% 29,6% 0,0% 70,0% 30,0% 25,0% 25,0% 50,0% 100,0% 0,0% 0,0% 50,0% 12,5% 37,5% Identifying how medical AI affects public and personal values of diverse groups. 27 15 3 9 0 10 4 3 3 0 4 4 0 0 0 5 4 0 1 0 8 3 0 5 55,6% 11,1% 33,3% 40,0% 30,0% 30,0% 100,0% 0,0% 0,0% 80,0% 0,0% 20,0% 37,5% 0,0% 62,5% Incorporating AI advancements in healthcare context. 26 10 9 7 1 9 0 6 3 1 4 4 0 0 0 5 4 0 1 0 8 2 3 3 38,5% 34,6% 26,9% 0,0% 66,7% 33,3% 100,0% 0,0% 0,0% 80,0% 0,0% 20,0% 25,0% 37,5% 37,5% 13 Monitoring and detecting problems and risks in AI systems before their release. 27 17 4 6 0 10 4 3 3 0 4 3 0 1 0 5 4 1 0 0 8 6 0 2 63,0% 14,8% 22,2% 40,0% 30,0% 30,0% 75,0% 0,0% 25,0% 80,0% 20,0% 0,0% 75,0% 0,0% 25,0% 14 Collaborating between different scientific disciplines. 27 14 4 9 0 10 2 3 5 0 4 4 0 0 0 5 5 0 0 0 8 3 1 4 51,9% 14,8% 33,3% 20,0% 30,0% 50,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 37,5% 12,5% 50,0% 15 Using a systems approach. 25 7 4 14 2 8 2 3 3 2 4 4 0 0 0 5 0 1 4 0 8 1 0 7 28,0% 16,0% 56,0% 25,0% 37,5% 37,5% 100,0% 0,0% 0,0% 0,0% 20,0% 80,0% 12,5% 0,0% 87,5% As a matter of principle, I don't think an AI tool should be used in the process of determining which topics are most pertinent. 25 4 16 5 2 9 1 6 2 1 4 0 3 1 0 4 1 3 0 1 8 2 4 2 16,0% 64,0% 20,0% 11,1% 66,7% 22,2% 0,0% 75,0% 25,0% 25,0% 75,0% 0,0% 25,0% 50,0% 25,0% Inventory and development of methods to include people in process of AI development and implementation 22 12 3 7 5 8 4 1 3 2 4 1 2 1 0 2 1 0 1 3 8 6 0 2 54,5% 13,6% 31,8% 50,0% 12,5% 37,5% 25,0% 50,0% 25,0% 50,0% 0,0% 50,0% 75,0% 0,0% 25,0% Adress AI inclusivity in different stages of the AI lifecycle 22 16 1 5 5 8 6 1 1 2 4 3 0 1 0 2 2 0 0 3 8 5 0 3 0 72,7% 4,5% 22,7% 75,0% 12,5% 12,5% 75,0% 0,0% 25,0% 100,0% 0,0% 0,0% 62,5% 0,0% 37,5% Adress AI inclusivity at different 'system' levels (micro, meso, macro) 22 16 1 5 5 8 6 1 1 2 4 3 0 1 0 2 1 0 1 3 8 6 0 2 0 72,7% 4,5% 22,7% 75,0% 12,5% 12,5% 75,0% 0,0% 25,0% 50,0% 0,0% 50,0% 75,0% 0,0% 25,0% Develop methods/frameworks to promote inclusivity for medical experts working with AI to increase safe adoption (e.g. AI literacy) 20 12 4 4 7 8 5 1 2 2 4 1 2 1 0 2 1 0 1 3 6 5 1 0 2 60,0% 20,0% 20,0% 62,5% 12,5% 25,0% 25,0% 50,0% 25,0% 50,0% 0,0% 50,0% 83,3% 16,7% 0,0% Make sure AI is at the service of patients 19 9 3 7 8 7 3 2 2 3 4 3 0 1 0 2 2 0 0 3 6 1 1 4 2 47,4% 15,8% 36,8% 42,9% 28,6% 28,6% 75,0% 0,0% 25,0% 100,0% 0,0% 0,0% 16,7% 16,7% 66,7% When developing and deploying healthcare AI, patients' rights should be taken into account. 6 4 1 1 21 1 0 1 0 9 0 0 0 0 4 1 1 0 0 4 4 3 0 1 4 66,7% 16,7% 16,7% 0,0% 100,0% 0,0% NA NA NA 100,0% 0,0% 0,0% 75,0% 0,0% 25,0%
Legend: The statements highlighted in grey represent those with the highest levels of agreement (defined as having over 66% of participants selecting "Agree" across the entire sample). Participants were asked to vote on the statements, considering what they felt were the most significant challenges we should focus on during the five-day in-person workshop. This means that when participants choose "Disagree" or "Pass", it is not necessarily because they disagree or are unsure about the statement, but because they felt it was not as big a priority to focus on the workshop as other statements. Among the 27 participants, four distinct opinion groups emerged in Pol.is. These groups were defined by two criteria: first, members within each group exhibited similar voting patterns across multiple statements; second, their voting behaviour was consistently different from that of participants in the other groups. Pol.is uses the k-means clustering algorithm to group participants into clusters and the silhouette coefficient to select an optimal number of clusters.
•
Participatory and co-design approaches • Representative and diverse data • Education and digital literacy • Fairness • Ethical and legal accountability • Real-world validation and feedback • Inclusive communication • Technical interoperability
Fig 1
Fig 1 describes the framework for the different stages of the workshop, based on the design
Fig 1 .
Fig 1. Structured workflow for the workshop. Adaptation of the double diamond model
survey responses, along with the initial 16 statements (two per theme) and the seven submitted anonymously by participants, drafted to inform the Pol.is deliberation. a New statements added by the participants. Participants of the workshop were asked to vote, through Pol.is, on each statement based on what they considered the most pressing challenges to address during the five-day in-person workshop (Insights stage).
1 . 6 .
Distinctive, addressing unique and complementary aspects of the workshop's overarching goal; 2. Balanced between human and technical dimensions, grounded in patient needs and lived experiences; 3. Interdisciplinary, suitable for engagement across diverse fields of expertise; 4. Aligned with the working definition of inclusive medical AI, reflecting the core pillars of ability, opportunity, and dignity for equitable health outcomes; 5. Actionable and impactful, enabling actionable insights and identifying research gaps; and Reflective of the PPI perspective, consistent with priorities identified by the PPI focus group. Three challenges with each a corresponding research question were then identified to guide the work and discussions of the participants during the in-person workshop (Insights stage): A. Integrating lived experiences and stakeholder voices across the AI lifecycle: How can we ensure that the lived experiences of diverse groups and stakeholder voices are systematically included throughout the AI lifecycle? B. Designing data collection practices to promote fairness and prevent inequalities: How can we ensure that data collection practices are representative, fair, and equitable and do not exacerbate inequalities while minimising biases in AI systems? C. Fostering regulatory frameworks to uphold human rights and promote inclusivity: How can we ensure that regulatory and ethical frameworks are used in AI development and implementation to safeguard human rights, address ethical concerns, promote trust, and support inclusive practices across the AI lifecycle?
process culminated in a final phase of the PREFER-IT framework consolidation based on the thematic analysis and clustering of the workshop data. The collective insights and results from the in-person workshop are summarised in Fig 2.
Fig 2 .
Fig 2. Results of the workshop summarised by day (D). The collective insights and main
Fig 3 .
Fig 3. PREFER-IT framework. How PREFER-IT originated from the thematic clusters that
Fig 4 each component of the PREFER-IT framework aligns with specific structural layers and phases of the medical AI lifecycle. For instance, Participatory and co-design approaches (P) are essential throughout all stages of the AI lifecycle and must actively involve all stakeholders (what is labelled as "humans"). Representative and diverse data (R) plays a crucial role within the data layer, particularly during the design phase. Elements such as Education and digital literacy (E), Ethical and legal accountability (E), and Inclusive communication (I) are fundamental governance considerations that span the entire AI lifecycle. Meanwhile, Fairness (F) and Technical interoperability (T) are core to the process layer and should be prioritised during development and validation. Finally, Real-world validation and feedback (R) are vital in any AI system, ensuring its adaptation to evolving socio-technical contexts, and should be considered during the validation, implementation and monitoring stages.
Fig 4 .
Fig 4. Implementation of the PREFER-IT framework across structural layers and the AI
Fig 5 .
Fig 5. Operationalising PREFER-IT. Mapping of the prototype solutions co-created during


=== INTRODUCTION ===

The advances and applications of artificial intelligence (AI) in healthcare, referred to in this paper as medical AI, have the potential to transform society. Medical AI could improve diagnosis, personalise treatment, increase operational efficiency, optimise resources such as staff time, equipment, and hospital capacity, and improve patients' experience [1][2][3][4][5]. Despite these possible benefits, medical AI carries known risks: it may reinforce existing biases against historically disadvantaged populations, underserved groups, and individuals experiencing (multiple) layers of vulnerability [6], and it remains prone to errors and patient harm [7][8][9]. If left unaddressed, these risks could ultimately exacerbate health disparities. [1]. Furthermore, medical AI raises questions regarding accessibility, autonomy, equity, human rights, and inclusivity [1]. Inclusivity is a recognised principle in healthcare, but concerns remain that AI may reproduce or exacerbate disparities across patient and population groups [7,10]. Groups such as older adults, ethnic minorities, and individuals with disabilities may face disproportionate risks or limited benefits from AI innovations [1,[10][11][12].
The literature shows a growing attention to issues of bias, trust, and fairness in AI [2,3,8,9,11,[13][14][15][16][17][18][19][20][21][22], with different disciplines addressing these issues from complementary perspectives. Medical informatics research prioritises algorithmic fairness metrics [7,9] and technical bias mitigation [16,23,24], focusing on the iterative phases of the AI lifecycle, which encompass the design, development, validation, implementation, and monitoring of AI [2], while often not addressing social determinants of health and inequity. Ethics, legal, and social science literature emphasise autonomy, justice, and human rights, frequently lacking operationalisation into technical or clinical practice [25][26][27]. Clinical and public health research addresses data representativeness, patient outcomes, and health disparities, often concentrated in high-income contexts [7,10,26].
While each field contributes valuable insights to these issues, these contributions have largely evolved in parallel, resulting in a fragmented landscape of efforts that, while individually valuable, collectively limit the development of cohesive and actionable solutions. This fragmentation results in uneven attention to people experiencing (multiple) layers of vulnerabilities [6] (e.g., more emphasis on race and gender bias than on age, disability or their intersections [12,28]) and gaps across the AI lifecycle, with deployment, monitoring, and governance being less explored than data collection and model design, for example [26].
Moreover, current approaches rarely engage with broader notions of inclusion, such as the social and structural contexts in which AI tools operate [12,29] or the systemic, participatory integration of marginalised voices and needs across the AI lifecycle.
To build on these diverse contributions, this paper adopts the lens of inclusive medical AI to examine how principles of inclusion can be systematically embedded into the AI lifecycle and governance of AI in healthcare.
We define medical inclusivity as the equal ability, opportunity and right of all individuals, particularly those who are underserved, vulnerable or otherwise disadvantaged based on identity and/or circumstances, to access health services, receive compassionate and highquality care, and achieve equitable health outcomes, in ways that respect human dignity. [13].
As a prerequisite for medical inclusivity, AI systems must be designed and implemented to ensure equitable access and use across all population groups, regardless of attributes such as age, sex, gender, income, race, ethnicity, sexual orientation, disability, or other characteristics protected by human rights principles [1]. Some works, although not specifically focused on healthcare, have attempted to address this gap by exploring diversity and inclusion by design [9,24,25] or by advocating for stakeholder engagement in design and evaluation [13,30]. For example, Zowghi and da Rimini propose five pillars (humans, data, process, system, governance) for embedding diversity and inclusion by design across AI ecosystems. This approach extends beyond fairness metrics by urging the inclusion of diverse voices, contexts, and institutions [30]. Wang and Blok propose a multilevel framework that shifts from AI micro-level issues (e.g., dataset bias, algorithm transparency) to structural concerns, from meso (clinical and organisational) to macro (systemic and socio-political) levels, which shape AI's broader impacts [25].
The FUTURE-AI guideline represents a first step, offering a structured, global consensus on trustworthy AI in healthcare across the AI lifecycle. While it addresses fairness, universality, and stakeholder engagement, its implementation of inclusivity remains limited: inclusivity is mainly viewed through bias mitigation and technical fairness metrics, rather than as a systematic design principle rooted in different contexts, lived experiences, and societal perspectives. [2] For healthcare specifically, the field remains fragmented, where valuable yet dispersed insights still need to be synthesised into a comprehensive, pragmatic framework for inclusive medical AI. While technical and governance-oriented guidelines exist [2], and ethical principles have been articulated in policy documents [1,11], systematic approaches to realise inclusivity remain dispersed and underdeveloped. General frameworks on inclusivity are not always transferable to healthcare, given the high-stakes and sensitive nature of this domain [27,32]. Medical AI tools have a direct impact on health outcomes, and the risks are substantial. Furthermore, healthcare is characterised by hierarchies and power dynamics, knowledge asymmetries between experts and patients, and the influence of professional and institutional norms [33][34][35]. These conditions can lead to the exclusion of voices and lived experiences, without explicit mechanisms for medical inclusivity.
This paper bridges this gap by presenting a set of co-created foundational requirements, named PREFER-IT, for the design, development, validation, implementation, monitoring, and governance of inclusive medical AI. Developed through a transdisciplinary co-creation process involving multiple disciplines and stakeholder perspectives, including patients, PREFER-IT provides a practical framework to realise medical AI inclusivity. In this paper, we describe how PREFER-IT was developed.

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

Objective  
Confirm whether the manuscript qualifies as a research paper or a systematic review based on structural and methodological characteristics.

Instructions  
1. Evaluate the manuscript for the following:
 - A research paper presents original research findings based on experiments, observations, or analyses conducted by the authors.
 - A systematic review provides a structured review of prior research following a clear methodology (e.g., inclusion/exclusion criteria, data synthesis).
2. For systematic reviews, look for indicators such as:
 - Mention of search strategies, search terms, or databases used.
 - Use of established frameworks like PRISMA, PICO, or equivalent (optional but useful indicators).
3. Check for presence of standard sections:
 - Abstract, Introduction, Methods, Results, Discussion/Conclusion.
 - Accept variants such as Background, Materials and Methods, Findings, or Conclusions.
4. Look for language suggesting original research or systematic methodology, such as:
 - “we conducted experiments,” “our findings show,” “we systematically reviewed,” “studies were selected based on.”
5. Mark the manuscript as FAIL if it lacks structural and methodological indicators of original research or systematic review.
- Narrative summaries, essays, or theory pieces without structured methodology should be marked FAIL.
6.  If it IS a research paper or a systematic review, mark as PASS. If it is NOT a research paper or a systematic review, mark as FAIL.

Output Format
- PASS / FAIL
- Detailed Explanation
- Context From the Paper

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

**SPECIFIC CHECK:**
Objective  
Confirm whether the manuscript qualifies as a research paper or a systematic review based on structural and methodological characteristics.

Instructions  
1. Evaluate the manuscript for the following:
 - A research paper presents original research findings based on experiments, observations, or analyses conducted by the authors.
 - A systematic review provides a structured review of prior research following a clear methodology (e.g., inclusion/exclusion criteria, data synthesis).
2. For systematic reviews, look for indicators such as:
 - Mention of search strategies, search terms, or databases used.
 - Use of established frameworks like PRISMA, PICO, or equivalent (optional but useful indicators).
3. Check for presence of standard sections:
 - Abstract, Introduction, Methods, Results, Discussion/Conclusion.
 - Accept variants such as Background, Materials and Methods, Findings, or Conclusions.
4. Look for language suggesting original research or systematic methodology, such as:
 - “we conducted experiments,” “our findings show,” “we systematically reviewed,” “studies were selected based on.”
5. Mark the manuscript as FAIL if it lacks structural and methodological indicators of original research or systematic review.
- Narrative summaries, essays, or theory pieces without structured methodology should be marked FAIL.
6.  If it IS a research paper or a systematic review, mark as PASS. If it is NOT a research paper or a systematic review, mark as FAIL.

Output Format
- PASS / FAIL
- Detailed Explanation
- Context From the Paper

**DOCUMENT CONTENT TO ANALYZE:**

=== ABSTRACT ===
Artificial intelligence (AI) in healthcare holds transformative potential but risks exacerbating existing health disparities if inclusivity is not explicitly accounted for. This study addresses the disconnected discussions on inclusive medical AI by developing a comprehensive framework, PREFER-IT. This framework is based on the outcomes of a five-day transdisciplinary cocreation workshop that involved 37 experts from diverse backgrounds, including healthcare, ethics, law, social sciences, AI, and patient advocacy. For this workshop, we used design thinking and participatory methodologies to develop a framework for realising inclusive medical AI. We identified three key challenges for realising inclusive medical AI: integrating the lived experiences and stakeholder voices across the AI lifecycle, designing data collection practices that promote fairness and prevent inequalities, and fostering regulatory frameworks to uphold human rights and promote inclusivity. The analysis of participants' perspectives informed the development of eight key thematic clusters of PREFER-IT: Participatory and codesign approaches (P), Representative and diverse data (R), Education and digital literacy (E), Fairness (F), Ethical and legal accountability (E), Real-world validation and feedback (R), Inclusive communication (I), and Technical interoperability (T). These elements were mapped across structural layers of AI (humans, data, system, process, and governance) and the AI lifecycle to guide inclusive design, development, validation, implementation, monitoring, and governance. This framework fosters stakeholder engagement and systemic change, positioning inclusion as a guiding principle in practice. PREFER-IT offers a practical and conceptual contribution for how to include ethical, legal and societal aspects when aiming to foster responsible and inclusive AI in healthcare.


=== KEYWORDS ===
PPF|| SSL|| WB|| SdK|| CL|| LB|| MP||Data Curation: PPF|| SSL|| WB|| LB||Investigation: PPF|| SSL|| WB|| JG|| LB|| MP||Methodology: PPF|| SSL|| WB|| JG|| LB|| MP||Supervision: SdK|| CL|| MP||Visualisation: PPF||Writing -Original Draft Preparation: PPF||Writing -Review & Editing: all the authors


=== TITLE ===
PREFER-IT: A transdisciplinary co-created framework to realise inclusive medical AI


=== SUPPLEMENTARY ===

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


=== DISCUSSION ===

This paper presents PREFER-IT, a set of co-created foundational requirements developed through a transdisciplinary workshop to provide an operational framework for realising inclusivity throughout the AI lifecycle and governance in healthcare. By engaging a diverse group of stakeholders in a structured co-creation process, we translated lived experiences and expert insights into actionable requirements for inclusive medical AI.
The workshop combined a preparatory PPI meeting and survey, a consensus-building exercise, and design thinking methods, including patient personas and journey mapping with the active involvement of patient experts. This structured process progressively moved from the broad challenge of inclusive medical AI to a set of concrete, experience-informed solutions and requirements, eventually culminating in the development of the PREFER-IT framework.
While existing frameworks often address fairness, trustworthiness or ethics in medical AI [2,9,18,25,[43][44][45][46], they generally remain conceptual or focus on technical dimensions. A unified, actionable framework for AI inclusivity, particularly within healthcare, remains absent, at least to our knowledge. PREFER-IT is a first step to respond to this gap by offering both a conceptual and applied foundation to guide the inclusive design, development, validation, implementation, monitoring, and governance of medical AI.
The co-creation process involved a small number of participants, providing an in-depth and explorative knowledge and exchange that generated valuable insights. The diversity of expertise and richness of perspectives provide a robust qualitative foundation for further empirical investigation. The co-created solutions, grounded in patient voices and lived experiences, particularly emphasised the human and governance dimensions of AI, reflecting the workshop's interdisciplinary composition across social sciences, ethics, and health policy.
However, the framework remains adaptable to technical domains and can guide integration at the data, process, and system levels of medical AI. While the discussions were primarily situated within a European context, this framing offers a strong basis for extending and validating the framework in other settings. A possible next step could be to test and adapt the framework across diverse contexts to capture a broader range of epistemic perspectives and societal concerns, ensuring its global relevance and applicability.
The workshop also revealed the importance of authentic participation. While patient involvement enriched the framework, it also highlighted risks of tokenism. Similar tensions have been observed in participatory AI design more broadly, where researchers and practitioners often struggle to reconcile participatory ambitions with practical constraints such as time, resources, and institutional norms [47]. To navigate these challenges, teams frequently rely on proxies (like stand-ins for stakeholders) that may inadvertently reinforce existing power asymmetries rather than dismantle them [47]. This underscores the need for co-creation to extend beyond isolated workshops toward sustained, reflexive engagement mechanisms that distribute decision-making authority and epistemic legitimacy. Without such safeguards, inclusion risks being symbolic rather than transformative.
Participants also identified several persistent gaps for realising inclusive medical AI. Rather than obstacles, these can be reframed as priority areas where PREFER-IT should be actively applied or advocated. For instance, the call for support tools to facilitate guideline adoption and compliance highlights the need for stronger public engagement in shaping and implementing AI regulation, as illustrated by the Algorithm Register of the Dutch government [48]. At the policy level, incorporating PREFER-IT principles into funding criteria, regulatory assessments, and institutional governance could help move inclusion from aspiration to routine practice. This approach would shape AI medical inclusivity in what is developed, how it is developed, who develops it, and how it is evaluated and refined over time.
Likewise, the demand for educational and capacity-building initiatives, such as patient training programs or education tools, aligns with ongoing policy efforts, such as those outlined in the AI Act [49], to support sustained implementation of PREFER-IT. Capacity-building should also target AI developers and healthcare professionals to bridge disciplinary gaps. As AI researchers often lack medical expertise, collaboration with clinicians and patients is crucial for understanding the clinical context and translating patient needs into technical design requirements. The more systemic concerns, including implementation complexity, safeguarding ethics, and balancing diverse values, highlight that PREFER-IT should not be approached as a simple checklist. Instead, it needs to be an adaptable infrastructure spanning the entire AI lifecycle and its various structural layers. The core challenge lies not in the absence of ethical discourse but in translating principles into actionable, context-sensitive mechanisms that can keep pace with technological innovation. Viewing inclusivity as a behavioural process that requires capability, opportunity, and motivation could strengthen the framework's practical applicability and promote organisational cultures that sustain inclusive AI development. [50] Looking ahead, the next steps involve translating PREFER-IT from a co-created framework into a living infrastructure for inclusive medical AI. This will require ongoing engagement with diverse stakeholders, including patients with intersectional vulnerabilities, regulators, and insurers. It also involves iterative testing in real-world projects, systematic evaluation across healthcare contexts, and continuous refinement based on user and patient feedback. Through this operationalisation, PREFER-IT aims to advance AI in healthcare that is not only effective but also equitable, ethical, legal, and socially responsible [51]. The co-creation process highlighted that realising inclusivity in medical AI requires more than just design features, depending on systemic enablers across multiple levels. In line with Wang and Blok's multi-level framework [25], PREFER-IT can be operationalised at the micro-level (individual AI issues), meso-level (organisational and systemic issues), and macro-level (philosophical issues). For example, policymakers can mandate transparency and equity audits; developers can integrate inclusion-by-design and participatory testing; researchers and clinicians can promote representative study designs; and patients and communities can codefine research priorities and evaluation metrics. The PREFER-IT framework could provide a basis for coordinating these actions across roles and governance levels. Finally, our research suggests a broader reframing: from an innovation-first logic to an inclusive-by-design paradigm. This shift positions inclusivity as a constitutive element of medical AI, embedded not only in outcomes but also in processes, governance structures, and power relations. By doing so, PREFER-IT helps address epistemic hierarchies in knowledge generation, challenging the dominance of quantitative and technical evidence and elevating the value of qualitative insights, experiential knowledge, and patient voices in shaping AI development. Through the development of PREFER-IT, we offer both a conceptual contribution and a practical resource to support stakeholders in realising inclusivity throughout the AI lifecycle in healthcare. The framework builds on, but also extends beyond, existing frameworks by positioning inclusion not as a peripheral ethical concern but as a core design principle, rooted in equity, stakeholder engagement, and governance accountability. Crucially, this process revealed that advancing inclusion in medical AI is not merely a matter of technological or design choices. It requires reconfiguring institutional logics, incentive structures, and policy environments. The PREFER-IT framework captures these multi-level dynamics and provides a framework for coordinated action. By explicitly incorporating the roles of patients and communities, it helps bridge the gap between AI development and implementation and the lived realities of those most affected by it. Ultimately, this process challenges dominant epistemic practices and paradigms in medical innovation, which often prioritise efficiency and technical advancement over equity and participation. Co-creation, as adopted here, is not merely a methodological technique but a stance that recognises the value of inclusion. It shifts the focus from innovation for to innovation with communities. 14,8% 18,5% 50,0% 20,0% 30,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 50,0% 25,0% 25,0% 1 Understanding the effects of bias on AI outputs, such as unfair recommendations. 27 15 3 9 0 10 2 3 5 0 4 4 0 0 0 5 5 0 0 0 8 4 0 4 0 55,6% 11,1% 33,3% 20,0% 30,0% 50,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 50,0% 0,0% 50,0% 2 Including diverse stakeholder voices throughout the AI lifecycle. 26 21 1 4 1 9 7 0 2 1 4 3 1 0 0 5 5 0 0 0 8 6 0 2 0 80,8% 3,8% 15,4% 77,8% 0,0% 22,2% 75,0% 25,0% 0,0% 100,0% 0,0% 0,0% 75,0% 0,0% 25,0% 3 Including the lived experiences of diverse groups in AI design and implementation. 27 22 0 5 0 10 8 0 2 0 4 4 0 0 0 5 5 0 0 0 8 5 0 3 0 81,5% 0,0% 18,5% 80,0% 0,0% 20,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 62,5% 0,0% 37,5% 4 Developing regulatory frameworks that address inclusivity in medical AI. 26 15 4 7 1 9 4 3 2 1 4 1 1 2 0 5 5 0 0 0 8 5 0 3 0 57,7% 15,4% 26,9% 44,4% 33,3% 22,2% 25,0% 25,0% 50,0% 100,0% 0,0% 0,0% 62,5% 0,0% 37,5% 5 Creating regulatory frameworks interpretable for all stakeholders. 27 10 9 8 0 10 0 6 4 0 4 0 2 2 0 5 5 0 0 0 8 5 1 2 0 37,0% 33,3% 29,6% 0,0% 60,0% 40,0% 0,0% 50,0% 50,0% 100,0% 0,0% 0,0% 62,5% 12,5% 25,0% 6 Ensuring training datasets represent diverse groups. 27 18 3 6 0 10 5 2 3 0 4 2 1 1 0 5 5 0 0 0 8 6 0 2 0 66,7% 11,1% 22,2% 50,0% 20,0% 30,0% 50,0% 25,0% 25,0% 100,0% 0,0% 0,0% 75,0% 0,0% 25,0% Assessing whether current data collection practices exacerbate inequalities.
27 20 3 4 0 10 5 3 2 0 4 4 0 0 0 5 5 0 0 0 8 6 0 2 0 74,1%
11,1% 14,8% 50,0% 30,0% 20,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 75,0% 0,0% 25,0% Investigating the societal impact of AI systems on diverse groups. 27 17 5 5 0 10 4 4 2 0 4 4 0 0 0 5 5 0 0 0 8 4 1 3 63,0% 18,5% 18,5% 40,0% 40,0% 20,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 50,0% 12,5% 37,5% 9 Continuous evaluation of AI across diverse groups. 26 13 4 9 1 9 4 3 2 1 4 1 1 2 0 5 5 0 0 0 8 3 0 5 50,0% 15,4% 34,6% 44,4% 33,3% 22,2% 25,0% 25,0% 50,0% 100,0% 0,0% 0,0% 37,5% 0,0% 62,5% 10 Identifying how medical AI affects human rights of diverse groups. 27 10 9 8 0 10 0 7 3 0 4 1 1 2 0 5 5 0 0 0 8 4 1 3 37,0% 33,3% 29,6% 0,0% 70,0% 30,0% 25,0% 25,0% 50,0% 100,0% 0,0% 0,0% 50,0% 12,5% 37,5% Identifying how medical AI affects public and personal values of diverse groups. 27 15 3 9 0 10 4 3 3 0 4 4 0 0 0 5 4 0 1 0 8 3 0 5 55,6% 11,1% 33,3% 40,0% 30,0% 30,0% 100,0% 0,0% 0,0% 80,0% 0,0% 20,0% 37,5% 0,0% 62,5% Incorporating AI advancements in healthcare context. 26 10 9 7 1 9 0 6 3 1 4 4 0 0 0 5 4 0 1 0 8 2 3 3 38,5% 34,6% 26,9% 0,0% 66,7% 33,3% 100,0% 0,0% 0,0% 80,0% 0,0% 20,0% 25,0% 37,5% 37,5% 13 Monitoring and detecting problems and risks in AI systems before their release. 27 17 4 6 0 10 4 3 3 0 4 3 0 1 0 5 4 1 0 0 8 6 0 2 63,0% 14,8% 22,2% 40,0% 30,0% 30,0% 75,0% 0,0% 25,0% 80,0% 20,0% 0,0% 75,0% 0,0% 25,0% 14 Collaborating between different scientific disciplines. 27 14 4 9 0 10 2 3 5 0 4 4 0 0 0 5 5 0 0 0 8 3 1 4 51,9% 14,8% 33,3% 20,0% 30,0% 50,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 37,5% 12,5% 50,0% 15 Using a systems approach. 25 7 4 14 2 8 2 3 3 2 4 4 0 0 0 5 0 1 4 0 8 1 0 7 28,0% 16,0% 56,0% 25,0% 37,5% 37,5% 100,0% 0,0% 0,0% 0,0% 20,0% 80,0% 12,5% 0,0% 87,5% As a matter of principle, I don't think an AI tool should be used in the process of determining which topics are most pertinent. 25 4 16 5 2 9 1 6 2 1 4 0 3 1 0 4 1 3 0 1 8 2 4 2 16,0% 64,0% 20,0% 11,1% 66,7% 22,2% 0,0% 75,0% 25,0% 25,0% 75,0% 0,0% 25,0% 50,0% 25,0% Inventory and development of methods to include people in process of AI development and implementation 22 12 3 7 5 8 4 1 3 2 4 1 2 1 0 2 1 0 1 3 8 6 0 2 54,5% 13,6% 31,8% 50,0% 12,5% 37,5% 25,0% 50,0% 25,0% 50,0% 0,0% 50,0% 75,0% 0,0% 25,0% Adress AI inclusivity in different stages of the AI lifecycle 22 16 1 5 5 8 6 1 1 2 4 3 0 1 0 2 2 0 0 3 8 5 0 3 0 72,7% 4,5% 22,7% 75,0% 12,5% 12,5% 75,0% 0,0% 25,0% 100,0% 0,0% 0,0% 62,5% 0,0% 37,5% Adress AI inclusivity at different 'system' levels (micro, meso, macro) 22 16 1 5 5 8 6 1 1 2 4 3 0 1 0 2 1 0 1 3 8 6 0 2 0 72,7% 4,5% 22,7% 75,0% 12,5% 12,5% 75,0% 0,0% 25,0% 50,0% 0,0% 50,0% 75,0% 0,0% 25,0% Develop methods/frameworks to promote inclusivity for medical experts working with AI to increase safe adoption (e.g. AI literacy) 20 12 4 4 7 8 5 1 2 2 4 1 2 1 0 2 1 0 1 3 6 5 1 0 2 60,0% 20,0% 20,0% 62,5% 12,5% 25,0% 25,0% 50,0% 25,0% 50,0% 0,0% 50,0% 83,3% 16,7% 0,0% Make sure AI is at the service of patients 19 9 3 7 8 7 3 2 2 3 4 3 0 1 0 2 2 0 0 3 6 1 1 4 2 47,4% 15,8% 36,8% 42,9% 28,6% 28,6% 75,0% 0,0% 25,0% 100,0% 0,0% 0,0% 16,7% 16,7% 66,7% When developing and deploying healthcare AI, patients' rights should be taken into account. 6 4 1 1 21 1 0 1 0 9 0 0 0 0 4 1 1 0 0 4 4 3 0 1 4 66,7% 16,7% 16,7% 0,0% 100,0% 0,0% NA NA NA 100,0% 0,0% 0,0% 75,0% 0,0% 25,0%
Legend: The statements highlighted in grey represent those with the highest levels of agreement (defined as having over 66% of participants selecting "Agree" across the entire sample). Participants were asked to vote on the statements, considering what they felt were the most significant challenges we should focus on during the five-day in-person workshop. This means that when participants choose "Disagree" or "Pass", it is not necessarily because they disagree or are unsure about the statement, but because they felt it was not as big a priority to focus on the workshop as other statements. Among the 27 participants, four distinct opinion groups emerged in Pol.is. These groups were defined by two criteria: first, members within each group exhibited similar voting patterns across multiple statements; second, their voting behaviour was consistently different from that of participants in the other groups. Pol.is uses the k-means clustering algorithm to group participants into clusters and the silhouette coefficient to select an optimal number of clusters.
•
Participatory and co-design approaches • Representative and diverse data • Education and digital literacy • Fairness • Ethical and legal accountability • Real-world validation and feedback • Inclusive communication • Technical interoperability
Fig 1
Fig 1 describes the framework for the different stages of the workshop, based on the design
Fig 1 .
Fig 1. Structured workflow for the workshop. Adaptation of the double diamond model
survey responses, along with the initial 16 statements (two per theme) and the seven submitted anonymously by participants, drafted to inform the Pol.is deliberation. a New statements added by the participants. Participants of the workshop were asked to vote, through Pol.is, on each statement based on what they considered the most pressing challenges to address during the five-day in-person workshop (Insights stage).
1 . 6 .
Distinctive, addressing unique and complementary aspects of the workshop's overarching goal; 2. Balanced between human and technical dimensions, grounded in patient needs and lived experiences; 3. Interdisciplinary, suitable for engagement across diverse fields of expertise; 4. Aligned with the working definition of inclusive medical AI, reflecting the core pillars of ability, opportunity, and dignity for equitable health outcomes; 5. Actionable and impactful, enabling actionable insights and identifying research gaps; and Reflective of the PPI perspective, consistent with priorities identified by the PPI focus group. Three challenges with each a corresponding research question were then identified to guide the work and discussions of the participants during the in-person workshop (Insights stage): A. Integrating lived experiences and stakeholder voices across the AI lifecycle: How can we ensure that the lived experiences of diverse groups and stakeholder voices are systematically included throughout the AI lifecycle? B. Designing data collection practices to promote fairness and prevent inequalities: How can we ensure that data collection practices are representative, fair, and equitable and do not exacerbate inequalities while minimising biases in AI systems? C. Fostering regulatory frameworks to uphold human rights and promote inclusivity: How can we ensure that regulatory and ethical frameworks are used in AI development and implementation to safeguard human rights, address ethical concerns, promote trust, and support inclusive practices across the AI lifecycle?
process culminated in a final phase of the PREFER-IT framework consolidation based on the thematic analysis and clustering of the workshop data. The collective insights and results from the in-person workshop are summarised in Fig 2.
Fig 2 .
Fig 2. Results of the workshop summarised by day (D). The collective insights and main
Fig 3 .
Fig 3. PREFER-IT framework. How PREFER-IT originated from the thematic clusters that
Fig 4 each component of the PREFER-IT framework aligns with specific structural layers and phases of the medical AI lifecycle. For instance, Participatory and co-design approaches (P) are essential throughout all stages of the AI lifecycle and must actively involve all stakeholders (what is labelled as "humans"). Representative and diverse data (R) plays a crucial role within the data layer, particularly during the design phase. Elements such as Education and digital literacy (E), Ethical and legal accountability (E), and Inclusive communication (I) are fundamental governance considerations that span the entire AI lifecycle. Meanwhile, Fairness (F) and Technical interoperability (T) are core to the process layer and should be prioritised during development and validation. Finally, Real-world validation and feedback (R) are vital in any AI system, ensuring its adaptation to evolving socio-technical contexts, and should be considered during the validation, implementation and monitoring stages.
Fig 4 .
Fig 4. Implementation of the PREFER-IT framework across structural layers and the AI
Fig 5 .
Fig 5. Operationalising PREFER-IT. Mapping of the prototype solutions co-created during


=== INTRODUCTION ===

The advances and applications of artificial intelligence (AI) in healthcare, referred to in this paper as medical AI, have the potential to transform society. Medical AI could improve diagnosis, personalise treatment, increase operational efficiency, optimise resources such as staff time, equipment, and hospital capacity, and improve patients' experience [1][2][3][4][5]. Despite these possible benefits, medical AI carries known risks: it may reinforce existing biases against historically disadvantaged populations, underserved groups, and individuals experiencing (multiple) layers of vulnerability [6], and it remains prone to errors and patient harm [7][8][9]. If left unaddressed, these risks could ultimately exacerbate health disparities. [1]. Furthermore, medical AI raises questions regarding accessibility, autonomy, equity, human rights, and inclusivity [1]. Inclusivity is a recognised principle in healthcare, but concerns remain that AI may reproduce or exacerbate disparities across patient and population groups [7,10]. Groups such as older adults, ethnic minorities, and individuals with disabilities may face disproportionate risks or limited benefits from AI innovations [1,[10][11][12].
The literature shows a growing attention to issues of bias, trust, and fairness in AI [2,3,8,9,11,[13][14][15][16][17][18][19][20][21][22], with different disciplines addressing these issues from complementary perspectives. Medical informatics research prioritises algorithmic fairness metrics [7,9] and technical bias mitigation [16,23,24], focusing on the iterative phases of the AI lifecycle, which encompass the design, development, validation, implementation, and monitoring of AI [2], while often not addressing social determinants of health and inequity. Ethics, legal, and social science literature emphasise autonomy, justice, and human rights, frequently lacking operationalisation into technical or clinical practice [25][26][27]. Clinical and public health research addresses data representativeness, patient outcomes, and health disparities, often concentrated in high-income contexts [7,10,26].
While each field contributes valuable insights to these issues, these contributions have largely evolved in parallel, resulting in a fragmented landscape of efforts that, while individually valuable, collectively limit the development of cohesive and actionable solutions. This fragmentation results in uneven attention to people experiencing (multiple) layers of vulnerabilities [6] (e.g., more emphasis on race and gender bias than on age, disability or their intersections [12,28]) and gaps across the AI lifecycle, with deployment, monitoring, and governance being less explored than data collection and model design, for example [26].
Moreover, current approaches rarely engage with broader notions of inclusion, such as the social and structural contexts in which AI tools operate [12,29] or the systemic, participatory integration of marginalised voices and needs across the AI lifecycle.
To build on these diverse contributions, this paper adopts the lens of inclusive medical AI to examine how principles of inclusion can be systematically embedded into the AI lifecycle and governance of AI in healthcare.
We define medical inclusivity as the equal ability, opportunity and right of all individuals, particularly those who are underserved, vulnerable or otherwise disadvantaged based on identity and/or circumstances, to access health services, receive compassionate and highquality care, and achieve equitable health outcomes, in ways that respect human dignity. [13].
As a prerequisite for medical inclusivity, AI systems must be designed and implemented to ensure equitable access and use across all population groups, regardless of attributes such as age, sex, gender, income, race, ethnicity, sexual orientation, disability, or other characteristics protected by human rights principles [1]. Some works, although not specifically focused on healthcare, have attempted to address this gap by exploring diversity and inclusion by design [9,24,25] or by advocating for stakeholder engagement in design and evaluation [13,30]. For example, Zowghi and da Rimini propose five pillars (humans, data, process, system, governance) for embedding diversity and inclusion by design across AI ecosystems. This approach extends beyond fairness metrics by urging the inclusion of diverse voices, contexts, and institutions [30]. Wang and Blok propose a multilevel framework that shifts from AI micro-level issues (e.g., dataset bias, algorithm transparency) to structural concerns, from meso (clinical and organisational) to macro (systemic and socio-political) levels, which shape AI's broader impacts [25].
The FUTURE-AI guideline represents a first step, offering a structured, global consensus on trustworthy AI in healthcare across the AI lifecycle. While it addresses fairness, universality, and stakeholder engagement, its implementation of inclusivity remains limited: inclusivity is mainly viewed through bias mitigation and technical fairness metrics, rather than as a systematic design principle rooted in different contexts, lived experiences, and societal perspectives. [2] For healthcare specifically, the field remains fragmented, where valuable yet dispersed insights still need to be synthesised into a comprehensive, pragmatic framework for inclusive medical AI. While technical and governance-oriented guidelines exist [2], and ethical principles have been articulated in policy documents [1,11], systematic approaches to realise inclusivity remain dispersed and underdeveloped. General frameworks on inclusivity are not always transferable to healthcare, given the high-stakes and sensitive nature of this domain [27,32]. Medical AI tools have a direct impact on health outcomes, and the risks are substantial. Furthermore, healthcare is characterised by hierarchies and power dynamics, knowledge asymmetries between experts and patients, and the influence of professional and institutional norms [33][34][35]. These conditions can lead to the exclusion of voices and lived experiences, without explicit mechanisms for medical inclusivity.
This paper bridges this gap by presenting a set of co-created foundational requirements, named PREFER-IT, for the design, development, validation, implementation, monitoring, and governance of inclusive medical AI. Developed through a transdisciplinary co-creation process involving multiple disciplines and stakeholder perspectives, including patients, PREFER-IT provides a practical framework to realise medical AI inclusivity. In this paper, we describe how PREFER-IT was developed.

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents original research findings from a transdisciplinary co‑creation workshop that led to the development of the PREFER‑IT framework. It includes a clear methods description (workshop design, participatory techniques, thematic analysis) and reports results (framework components), satisfying the criteria for a research paper rather than a systematic review.  
**CONTEXT:**  
- “This study addresses the disconnected discussions on inclusive medical AI by developing a comprehensive framework, PREFER‑IT. … This framework is based on the outcomes of a five‑day transdisciplinary cocreation workshop …”  
- “The workshop combined a preparatory PPI meeting and survey, a consensus‑building exercise, and design thinking methods …”  
- “The co‑creation process involved a small number of participants … the framework remains adaptable to technical domains …”  
- The manuscript contains standard research sections (Abstract, Introduction, Discussion) and a methods‑like description of the workshop, indicating original empirical work.

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents original research findings from a transdisciplinary workshop that co‑created the PREFER‑IT framework. It includes a structured methodology (workshop design, participatory methods, thematic analysis) and reports results (framework components, thematic clusters). These are characteristic of a research paper, not a systematic review.  
**CONTEXT:**  
- “This study addresses the disconnected discussions on inclusive medical AI by developing a comprehensive framework, PREFER‑IT… based on the outcomes of a five‑day transdisciplinary cocreation workshop…” (Abstract)  
- “The workshop combined a preparatory PPI meeting and survey, a consensus‑building exercise, and design thinking methods…” (Discussion)  
- “The co‑creation process involved a small number of participants… the framework remains adaptable to technical domains…” (Discussion)  
These sections demonstrate original empirical work rather than a systematic literature synthesis.

=== DISCUSSION ===

This paper presents PREFER-IT, a set of co-created foundational requirements developed through a transdisciplinary workshop to provide an operational framework for realising inclusivity throughout the AI lifecycle and governance in healthcare. By engaging a diverse group of stakeholders in a structured co-creation process, we translated lived experiences and expert insights into actionable requirements for inclusive medical AI.
The workshop combined a preparatory PPI meeting and survey, a consensus-building exercise, and design thinking methods, including patient personas and journey mapping with the active involvement of patient experts. This structured process progressively moved from the broad challenge of inclusive medical AI to a set of concrete, experience-informed solutions and requirements, eventually culminating in the development of the PREFER-IT framework.
While existing frameworks often address fairness, trustworthiness or ethics in medical AI [2,9,18,25,[43][44][45][46], they generally remain conceptual or focus on technical dimensions. A unified, actionable framework for AI inclusivity, particularly within healthcare, remains absent, at least to our knowledge. PREFER-IT is a first step to respond to this gap by offering both a conceptual and applied foundation to guide the inclusive design, development, validation, implementation, monitoring, and governance of medical AI.
The co-creation process involved a small number of participants, providing an in-depth and explorative knowledge and exchange that generated valuable insights. The diversity of expertise and richness of perspectives provide a robust qualitative foundation for further empirical investigation. The co-created solutions, grounded in patient voices and lived experiences, particularly emphasised the human and governance dimensions of AI, reflecting the workshop's interdisciplinary composition across social sciences, ethics, and health policy.
However, the framework remains adaptable to technical domains and can guide integration at the data, process, and system levels of medical AI. While the discussions were primarily situated within a European context, this framing offers a strong basis for extending and validating the framework in other settings. A possible next step could be to test and adapt the framework across diverse contexts to capture a broader range of epistemic perspectives and societal concerns, ensuring its global relevance and applicability.
The workshop also revealed the importance of authentic participation. While patient involvement enriched the framework, it also highlighted risks of tokenism. Similar tensions have been observed in participatory AI design more broadly, where researchers and practitioners often struggle to reconcile participatory ambitions with practical constraints such as time, resources, and institutional norms [47]. To navigate these challenges, teams frequently rely on proxies (like stand-ins for stakeholders) that may inadvertently reinforce existing power asymmetries rather than dismantle them [47]. This underscores the need for co-creation to extend beyond isolated workshops toward sustained, reflexive engagement mechanisms that distribute decision-making authority and epistemic legitimacy. Without such safeguards, inclusion risks being symbolic rather than transformative.
Participants also identified several persistent gaps for realising inclusive medical AI. Rather than obstacles, these can be reframed as priority areas where PREFER-IT should be actively applied or advocated. For instance, the call for support tools to facilitate guideline adoption and compliance highlights the need for stronger public engagement in shaping and implementing AI regulation, as illustrated by the Algorithm Register of the Dutch government [48]. At the policy level, incorporating PREFER-IT principles into funding criteria, regulatory assessments, and institutional governance could help move inclusion from aspiration to routine practice. This approach would shape AI medical inclusivity in what is developed, how it is developed, who develops it, and how it is evaluated and refined over time.
Likewise, the demand for educational and capacity-building initiatives, such as patient training programs or education tools, aligns with ongoing policy efforts, such as those outlined in the AI Act [49], to support sustained implementation of PREFER-IT. Capacity-building should also target AI developers and healthcare professionals to bridge disciplinary gaps. As AI researchers often lack medical expertise, collaboration with clinicians and patients is crucial for understanding the clinical context and translating patient needs into technical design requirements. The more systemic concerns, including implementation complexity, safeguarding ethics, and balancing diverse values, highlight that PREFER-IT should not be approached as a simple checklist. Instead, it needs to be an adaptable infrastructure spanning the entire AI lifecycle and its various structural layers. The core challenge lies not in the absence of ethical discourse but in translating principles into actionable, context-sensitive mechanisms that can keep pace with technological innovation. Viewing inclusivity as a behavioural process that requires capability, opportunity, and motivation could strengthen the framework's practical applicability and promote organisational cultures that sustain inclusive AI development. [50] Looking ahead, the next steps involve translating PREFER-IT from a co-created framework into a living infrastructure for inclusive medical AI. This will require ongoing engagement with diverse stakeholders, including patients with intersectional vulnerabilities, regulators, and insurers. It also involves iterative testing in real-world projects, systematic evaluation across healthcare contexts, and continuous refinement based on user and patient feedback. Through this operationalisation, PREFER-IT aims to advance AI in healthcare that is not only effective but also equitable, ethical, legal, and socially responsible [51]. The co-creation process highlighted that realising inclusivity in medical AI requires more than just design features, depending on systemic enablers across multiple levels. In line with Wang and Blok's multi-level framework [25], PREFER-IT can be operationalised at the micro-level (individual AI issues), meso-level (organisational and systemic issues), and macro-level (philosophical issues). For example, policymakers can mandate transparency and equity audits; developers can integrate inclusion-by-design and participatory testing; researchers and clinicians can promote representative study designs; and patients and communities can codefine research priorities and evaluation metrics. The PREFER-IT framework could provide a basis for coordinating these actions across roles and governance levels. Finally, our research suggests a broader reframing: from an innovation-first logic to an inclusive-by-design paradigm. This shift positions inclusivity as a constitutive element of medical AI, embedded not only in outcomes but also in processes, governance structures, and power relations. By doing so, PREFER-IT helps address epistemic hierarchies in knowledge generation, challenging the dominance of quantitative and technical evidence and elevating the value of qualitative insights, experiential knowledge, and patient voices in shaping AI development. Through the development of PREFER-IT, we offer both a conceptual contribution and a practical resource to support stakeholders in realising inclusivity throughout the AI lifecycle in healthcare. The framework builds on, but also extends beyond, existing frameworks by positioning inclusion not as a peripheral ethical concern but as a core design principle, rooted in equity, stakeholder engagement, and governance accountability. Crucially, this process revealed that advancing inclusion in medical AI is not merely a matter of technological or design choices. It requires reconfiguring institutional logics, incentive structures, and policy environments. The PREFER-IT framework captures these multi-level dynamics and provides a framework for coordinated action. By explicitly incorporating the roles of patients and communities, it helps bridge the gap between AI development and implementation and the lived realities of those most affected by it. Ultimately, this process challenges dominant epistemic practices and paradigms in medical innovation, which often prioritise efficiency and technical advancement over equity and participation. Co-creation, as adopted here, is not merely a methodological technique but a stance that recognises the value of inclusion. It shifts the focus from innovation for to innovation with communities. 14,8% 18,5% 50,0% 20,0% 30,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 50,0% 25,0% 25,0% 1 Understanding the effects of bias on AI outputs, such as unfair recommendations. 27 15 3 9 0 10 2 3 5 0 4 4 0 0 0 5 5 0 0 0 8 4 0 4 0 55,6% 11,1% 33,3% 20,0% 30,0% 50,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 50,0% 0,0% 50,0% 2 Including diverse stakeholder voices throughout the AI lifecycle. 26 21 1 4 1 9 7 0 2 1 4 3 1 0 0 5 5 0 0 0 8 6 0 2 0 80,8% 3,8% 15,4% 77,8% 0,0% 22,2% 75,0% 25,0% 0,0% 100,0% 0,0% 0,0% 75,0% 0,0% 25,0% 3 Including the lived experiences of diverse groups in AI design and implementation. 27 22 0 5 0 10 8 0 2 0 4 4 0 0 0 5 5 0 0 0 8 5 0 3 0 81,5% 0,0% 18,5% 80,0% 0,0% 20,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 62,5% 0,0% 37,5% 4 Developing regulatory frameworks that address inclusivity in medical AI. 26 15 4 7 1 9 4 3 2 1 4 1 1 2 0 5 5 0 0 0 8 5 0 3 0 57,7% 15,4% 26,9% 44,4% 33,3% 22,2% 25,0% 25,0% 50,0% 100,0% 0,0% 0,0% 62,5% 0,0% 37,5% 5 Creating regulatory frameworks interpretable for all stakeholders. 27 10 9 8 0 10 0 6 4 0 4 0 2 2 0 5 5 0 0 0 8 5 1 2 0 37,0% 33,3% 29,6% 0,0% 60,0% 40,0% 0,0% 50,0% 50,0% 100,0% 0,0% 0,0% 62,5% 12,5% 25,0% 6 Ensuring training datasets represent diverse groups. 27 18 3 6 0 10 5 2 3 0 4 2 1 1 0 5 5 0 0 0 8 6 0 2 0 66,7% 11,1% 22,2% 50,0% 20,0% 30,0% 50,0% 25,0% 25,0% 100,0% 0,0% 0,0% 75,0% 0,0% 25,0% Assessing whether current data collection practices exacerbate inequalities.
27 20 3 4 0 10 5 3 2 0 4 4 0 0 0 5 5 0 0 0 8 6 0 2 0 74,1%
11,1% 14,8% 50,0% 30,0% 20,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 75,0% 0,0% 25,0% Investigating the societal impact of AI systems on diverse groups. 27 17 5 5 0 10 4 4 2 0 4 4 0 0 0 5 5 0 0 0 8 4 1 3 63,0% 18,5% 18,5% 40,0% 40,0% 20,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 50,0% 12,5% 37,5% 9 Continuous evaluation of AI across diverse groups. 26 13 4 9 1 9 4 3 2 1 4 1 1 2 0 5 5 0 0 0 8 3 0 5 50,0% 15,4% 34,6% 44,4% 33,3% 22,2% 25,0% 25,0% 50,0% 100,0% 0,0% 0,0% 37,5% 0,0% 62,5% 10 Identifying how medical AI affects human rights of diverse groups. 27 10 9 8 0 10 0 7 3 0 4 1 1 2 0 5 5 0 0 0 8 4 1 3 37,0% 33,3% 29,6% 0,0% 70,0% 30,0% 25,0% 25,0% 50,0% 100,0% 0,0% 0,0% 50,0% 12,5% 37,5% Identifying how medical AI affects public and personal values of diverse groups. 27 15 3 9 0 10 4 3 3 0 4 4 0 0 0 5 4 0 1 0 8 3 0 5 55,6% 11,1% 33,3% 40,0% 30,0% 30,0% 100,0% 0,0% 0,0% 80,0% 0,0% 20,0% 37,5% 0,0% 62,5% Incorporating AI advancements in healthcare context. 26 10 9 7 1 9 0 6 3 1 4 4 0 0 0 5 4 0 1 0 8 2 3 3 38,5% 34,6% 26,9% 0,0% 66,7% 33,3% 100,0% 0,0% 0,0% 80,0% 0,0% 20,0% 25,0% 37,5% 37,5% 13 Monitoring and detecting problems and risks in AI systems before their release. 27 17 4 6 0 10 4 3 3 0 4 3 0 1 0 5 4 1 0 0 8 6 0 2 63,0% 14,8% 22,2% 40,0% 30,0% 30,0% 75,0% 0,0% 25,0% 80,0% 20,0% 0,0% 75,0% 0,0% 25,0% 14 Collaborating between different scientific disciplines. 27 14 4 9 0 10 2 3 5 0 4 4 0 0 0 5 5 0 0 0 8 3 1 4 51,9% 14,8% 33,3% 20,0% 30,0% 50,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 37,5% 12,5% 50,0% 15 Using a systems approach. 25 7 4 14 2 8 2 3 3 2 4 4 0 0 0 5 0 1 4 0 8 1 0 7 28,0% 16,0% 56,0% 25,0% 37,5% 37,5% 100,0% 0,0% 0,0% 0,0% 20,0% 80,0% 12,5% 0,0% 87,5% As a matter of principle, I don't think an AI tool should be used in the process of determining which topics are most pertinent. 25 4 16 5 2 9 1 6 2 1 4 0 3 1 0 4 1 3 0 1 8 2 4 2 16,0% 64,0% 20,0% 11,1% 66,7% 22,2% 0,0% 75,0% 25,0% 25,0% 75,0% 0,0% 25,0% 50,0% 25,0% Inventory and development of methods to include people in process of AI development and implementation 22 12 3 7 5 8 4 1 3 2 4 1 2 1 0 2 1 0 1 3 8 6 0 2 54,5% 13,6% 31,8% 50,0% 12,5% 37,5% 25,0% 50,0% 25,0% 50,0% 0,0% 50,0% 75,0% 0,0% 25,0% Adress AI inclusivity in different stages of the AI lifecycle 22 16 1 5 5 8 6 1 1 2 4 3 0 1 0 2 2 0 0 3 8 5 0 3 0 72,7% 4,5% 22,7% 75,0% 12,5% 12,5% 75,0% 0,0% 25,0% 100,0% 0,0% 0,0% 62,5% 0,0% 37,5% Adress AI inclusivity at different 'system' levels (micro, meso, macro) 22 16 1 5 5 8 6 1 1 2 4 3 0 1 0 2 1 0 1 3 8 6 0 2 0 72,7% 4,5% 22,7% 75,0% 12,5% 12,5% 75,0% 0,0% 25,0% 50,0% 0,0% 50,0% 75,0% 0,0% 25,0% Develop methods/frameworks to promote inclusivity for medical experts working with AI to increase safe adoption (e.g. AI literacy) 20 12 4 4 7 8 5 1 2 2 4 1 2 1 0 2 1 0 1 3 6 5 1 0 2 60,0% 20,0% 20,0% 62,5% 12,5% 25,0% 25,0% 50,0% 25,0% 50,0% 0,0% 50,0% 83,3% 16,7% 0,0% Make sure AI is at the service of patients 19 9 3 7 8 7 3 2 2 3 4 3 0 1 0 2 2 0 0 3 6 1 1 4 2 47,4% 15,8% 36,8% 42,9% 28,6% 28,6% 75,0% 0,0% 25,0% 100,0% 0,0% 0,0% 16,7% 16,7% 66,7% When developing and deploying healthcare AI, patients' rights should be taken into account. 6 4 1 1 21 1 0 1 0 9 0 0 0 0 4 1 1 0 0 4 4 3 0 1 4 66,7% 16,7% 16,7% 0,0% 100,0% 0,0% NA NA NA 100,0% 0,0% 0,0% 75,0% 0,0% 25,0%
Legend: The statements highlighted in grey represent those with the highest levels of agreement (defined as having over 66% of participants selecting "Agree" across the entire sample). Participants were asked to vote on the statements, considering what they felt were the most significant challenges we should focus on during the five-day in-person workshop. This means that when participants choose "Disagree" or "Pass", it is not necessarily because they disagree or are unsure about the statement, but because they felt it was not as big a priority to focus on the workshop as other statements. Among the 27 participants, four distinct opinion groups emerged in Pol.is. These groups were defined by two criteria: first, members within each group exhibited similar voting patterns across multiple statements; second, their voting behaviour was consistently different from that of participants in the other groups. Pol.is uses the k-means clustering algorithm to group participants into clusters and the silhouette coefficient to select an optimal number of clusters.
•
Participatory and co-design approaches • Representative and diverse data • Education and digital literacy • Fairness • Ethical and legal accountability • Real-world validation and feedback • Inclusive communication • Technical interoperability
Fig 1
Fig 1 describes the framework for the different stages of the workshop, based on the design
Fig 1 .
Fig 1. Structured workflow for the workshop. Adaptation of the double diamond model
survey responses, along with the initial 16 statements (two per theme) and the seven submitted anonymously by participants, drafted to inform the Pol.is deliberation. a New statements added by the participants. Participants of the workshop were asked to vote, through Pol.is, on each statement based on what they considered the most pressing challenges to address during the five-day in-person workshop (Insights stage).
1 . 6 .
Distinctive, addressing unique and complementary aspects of the workshop's overarching goal; 2. Balanced between human and technical dimensions, grounded in patient needs and lived experiences; 3. Interdisciplinary, suitable for engagement across diverse fields of expertise; 4. Aligned with the working definition of inclusive medical AI, reflecting the core pillars of ability, opportunity, and dignity for equitable health outcomes; 5. Actionable and impactful, enabling actionable insights and identifying research gaps; and Reflective of the PPI perspective, consistent with priorities identified by the PPI focus group. Three challenges with each a corresponding research question were then identified to guide the work and discussions of the participants during the in-person workshop (Insights stage): A. Integrating lived experiences and stakeholder voices across the AI lifecycle: How can we ensure that the lived experiences of diverse groups and stakeholder voices are systematically included throughout the AI lifecycle? B. Designing data collection practices to promote fairness and prevent inequalities: How can we ensure that data collection practices are representative, fair, and equitable and do not exacerbate inequalities while minimising biases in AI systems? C. Fostering regulatory frameworks to uphold human rights and promote inclusivity: How can we ensure that regulatory and ethical frameworks are used in AI development and implementation to safeguard human rights, address ethical concerns, promote trust, and support inclusive practices across the AI lifecycle?
process culminated in a final phase of the PREFER-IT framework consolidation based on the thematic analysis and clustering of the workshop data. The collective insights and results from the in-person workshop are summarised in Fig 2.
Fig 2 .
Fig 2. Results of the workshop summarised by day (D). The collective insights and main
Fig 3 .
Fig 3. PREFER-IT framework. How PREFER-IT originated from the thematic clusters that
Fig 4 each component of the PREFER-IT framework aligns with specific structural layers and phases of the medical AI lifecycle. For instance, Participatory and co-design approaches (P) are essential throughout all stages of the AI lifecycle and must actively involve all stakeholders (what is labelled as "humans"). Representative and diverse data (R) plays a crucial role within the data layer, particularly during the design phase. Elements such as Education and digital literacy (E), Ethical and legal accountability (E), and Inclusive communication (I) are fundamental governance considerations that span the entire AI lifecycle. Meanwhile, Fairness (F) and Technical interoperability (T) are core to the process layer and should be prioritised during development and validation. Finally, Real-world validation and feedback (R) are vital in any AI system, ensuring its adaptation to evolving socio-technical contexts, and should be considered during the validation, implementation and monitoring stages.
Fig 4 .
Fig 4. Implementation of the PREFER-IT framework across structural layers and the AI
Fig 5 .
Fig 5. Operationalising PREFER-IT. Mapping of the prototype solutions co-created during


=== INTRODUCTION ===

The advances and applications of artificial intelligence (AI) in healthcare, referred to in this paper as medical AI, have the potential to transform society. Medical AI could improve diagnosis, personalise treatment, increase operational efficiency, optimise resources such as staff time, equipment, and hospital capacity, and improve patients' experience [1][2][3][4][5]. Despite these possible benefits, medical AI carries known risks: it may reinforce existing biases against historically disadvantaged populations, underserved groups, and individuals experiencing (multiple) layers of vulnerability [6], and it remains prone to errors and patient harm [7][8][9]. If left unaddressed, these risks could ultimately exacerbate health disparities. [1]. Furthermore, medical AI raises questions regarding accessibility, autonomy, equity, human rights, and inclusivity [1]. Inclusivity is a recognised principle in healthcare, but concerns remain that AI may reproduce or exacerbate disparities across patient and population groups [7,10]. Groups such as older adults, ethnic minorities, and individuals with disabilities may face disproportionate risks or limited benefits from AI innovations [1,[10][11][12].
The literature shows a growing attention to issues of bias, trust, and fairness in AI [2,3,8,9,11,[13][14][15][16][17][18][19][20][21][22], with different disciplines addressing these issues from complementary perspectives. Medical informatics research prioritises algorithmic fairness metrics [7,9] and technical bias mitigation [16,23,24], focusing on the iterative phases of the AI lifecycle, which encompass the design, development, validation, implementation, and monitoring of AI [2], while often not addressing social determinants of health and inequity. Ethics, legal, and social science literature emphasise autonomy, justice, and human rights, frequently lacking operationalisation into technical or clinical practice [25][26][27]. Clinical and public health research addresses data representativeness, patient outcomes, and health disparities, often concentrated in high-income contexts [7,10,26].
While each field contributes valuable insights to these issues, these contributions have largely evolved in parallel, resulting in a fragmented landscape of efforts that, while individually valuable, collectively limit the development of cohesive and actionable solutions. This fragmentation results in uneven attention to people experiencing (multiple) layers of vulnerabilities [6] (e.g., more emphasis on race and gender bias than on age, disability or their intersections [12,28]) and gaps across the AI lifecycle, with deployment, monitoring, and governance being less explored than data collection and model design, for example [26].
Moreover, current approaches rarely engage with broader notions of inclusion, such as the social and structural contexts in which AI tools operate [12,29] or the systemic, participatory integration of marginalised voices and needs across the AI lifecycle.
To build on these diverse contributions, this paper adopts the lens of inclusive medical AI to examine how principles of inclusion can be systematically embedded into the AI lifecycle and governance of AI in healthcare.
We define medical inclusivity as the equal ability, opportunity and right of all individuals, particularly those who are underserved, vulnerable or otherwise disadvantaged based on identity and/or circumstances, to access health services, receive compassionate and highquality care, and achieve equitable health outcomes, in ways that respect human dignity. [13].
As a prerequisite for medical inclusivity, AI systems must be designed and implemented to ensure equitable access and use across all population groups, regardless of attributes such as age, sex, gender, income, race, ethnicity, sexual orientation, disability, or other characteristics protected by human rights principles [1]. Some works, although not specifically focused on healthcare, have attempted to address this gap by exploring diversity and inclusion by design [9,24,25] or by advocating for stakeholder engagement in design and evaluation [13,30]. For example, Zowghi and da Rimini propose five pillars (humans, data, process, system, governance) for embedding diversity and inclusion by design across AI ecosystems. This approach extends beyond fairness metrics by urging the inclusion of diverse voices, contexts, and institutions [30]. Wang and Blok propose a multilevel framework that shifts from AI micro-level issues (e.g., dataset bias, algorithm transparency) to structural concerns, from meso (clinical and organisational) to macro (systemic and socio-political) levels, which shape AI's broader impacts [25].
The FUTURE-AI guideline represents a first step, offering a structured, global consensus on trustworthy AI in healthcare across the AI lifecycle. While it addresses fairness, universality, and stakeholder engagement, its implementation of inclusivity remains limited: inclusivity is mainly viewed through bias mitigation and technical fairness metrics, rather than as a systematic design principle rooted in different contexts, lived experiences, and societal perspectives. [2] For healthcare specifically, the field remains fragmented, where valuable yet dispersed insights still need to be synthesised into a comprehensive, pragmatic framework for inclusive medical AI. While technical and governance-oriented guidelines exist [2], and ethical principles have been articulated in policy documents [1,11], systematic approaches to realise inclusivity remain dispersed and underdeveloped. General frameworks on inclusivity are not always transferable to healthcare, given the high-stakes and sensitive nature of this domain [27,32]. Medical AI tools have a direct impact on health outcomes, and the risks are substantial. Furthermore, healthcare is characterised by hierarchies and power dynamics, knowledge asymmetries between experts and patients, and the influence of professional and institutional norms [33][34][35]. These conditions can lead to the exclusion of voices and lived experiences, without explicit mechanisms for medical inclusivity.
This paper bridges this gap by presenting a set of co-created foundational requirements, named PREFER-IT, for the design, development, validation, implementation, monitoring, and governance of inclusive medical AI. Developed through a transdisciplinary co-creation process involving multiple disciplines and stakeholder perspectives, including patients, PREFER-IT provides a practical framework to realise medical AI inclusivity. In this paper, we describe how PREFER-IT was developed.


=== SUPPLEMENTARY ===

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


=== ABSTRACT ===
Artificial intelligence (AI) in healthcare holds transformative potential but risks exacerbating existing health disparities if inclusivity is not explicitly accounted for. This study addresses the disconnected discussions on inclusive medical AI by developing a comprehensive framework, PREFER-IT. This framework is based on the outcomes of a five-day transdisciplinary cocreation workshop that involved 37 experts from diverse backgrounds, including healthcare, ethics, law, social sciences, AI, and patient advocacy. For this workshop, we used design thinking and participatory methodologies to develop a framework for realising inclusive medical AI. We identified three key challenges for realising inclusive medical AI: integrating the lived experiences and stakeholder voices across the AI lifecycle, designing data collection practices that promote fairness and prevent inequalities, and fostering regulatory frameworks to uphold human rights and promote inclusivity. The analysis of participants' perspectives informed the development of eight key thematic clusters of PREFER-IT: Participatory and codesign approaches (P), Representative and diverse data (R), Education and digital literacy (E), Fairness (F), Ethical and legal accountability (E), Real-world validation and feedback (R), Inclusive communication (I), and Technical interoperability (T). These elements were mapped across structural layers of AI (humans, data, system, process, and governance) and the AI lifecycle to guide inclusive design, development, validation, implementation, monitoring, and governance. This framework fosters stakeholder engagement and systemic change, positioning inclusion as a guiding principle in practice. PREFER-IT offers a practical and conceptual contribution for how to include ethical, legal and societal aspects when aiming to foster responsible and inclusive AI in healthcare.


=== KEYWORDS ===
PPF|| SSL|| WB|| SdK|| CL|| LB|| MP||Data Curation: PPF|| SSL|| WB|| LB||Investigation: PPF|| SSL|| WB|| JG|| LB|| MP||Methodology: PPF|| SSL|| WB|| JG|| LB|| MP||Supervision: SdK|| CL|| MP||Visualisation: PPF||Writing -Original Draft Preparation: PPF||Writing -Review & Editing: all the authors


=== TITLE ===
PREFER-IT: A transdisciplinary co-created framework to realise inclusive medical AI

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

Objective  
Confirm whether the manuscript includes new data or qualifies as a clinical research protocol, based on content required by medRxiv.

Instructions 
1. Analyze the manuscript for the presence of either:
	- New data from experiments, observations, clinical trials, or simulations, even if based on previously published sources (e.g., meta-analyses with new synthesis).
	- For a medRxiv submission, check whether the submission is a clinical research protocol, defined as a structured plan for a clinical study.
2. Treat meta-analyses and data syntheses from prior studies as valid forms of ""data"" for this check, if they involve original re-analysis or synthesis.
3. To confirm presence of new data:
	- Review tables, figures, or data presentations that are not directly reused from prior published work.
	- Look for explicit mentions of new findings or original analysis.
4. To confirm a clinical research protocol:
	- Check for structured information on study objectives, design, methods, eligibility criteria, or trial phases.
	- Trial registration numbers or ethics review statements are useful indicators.
5. Mark the manuscript as FAIL if neither new data nor clinical protocol indicators are found.
	- If the content is ambiguous or borderline, mark as FAIL with a clear explanation so that a human screener can review the context.

Output
1. Provide a PASS or FAIL result
	- Has New Data Or Is A Clinical Research Protocol: PASS
	- Does Not Have New Data Or A Clinical Research Protocol: FAIL
2. Provide a Detailed Explanation to evidence the PASS or FAIL result
3. Provide the context From the Paper to evidence the PASS or FAIL result

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

**SPECIFIC CHECK:**
Objective  
Confirm whether the manuscript includes new data or qualifies as a clinical research protocol, based on content required by medRxiv.

Instructions 
1. Analyze the manuscript for the presence of either:
	- New data from experiments, observations, clinical trials, or simulations, even if based on previously published sources (e.g., meta-analyses with new synthesis).
	- For a medRxiv submission, check whether the submission is a clinical research protocol, defined as a structured plan for a clinical study.
2. Treat meta-analyses and data syntheses from prior studies as valid forms of ""data"" for this check, if they involve original re-analysis or synthesis.
3. To confirm presence of new data:
	- Review tables, figures, or data presentations that are not directly reused from prior published work.
	- Look for explicit mentions of new findings or original analysis.
4. To confirm a clinical research protocol:
	- Check for structured information on study objectives, design, methods, eligibility criteria, or trial phases.
	- Trial registration numbers or ethics review statements are useful indicators.
5. Mark the manuscript as FAIL if neither new data nor clinical protocol indicators are found.
	- If the content is ambiguous or borderline, mark as FAIL with a clear explanation so that a human screener can review the context.

Output
1. Provide a PASS or FAIL result
	- Has New Data Or Is A Clinical Research Protocol: PASS
	- Does Not Have New Data Or A Clinical Research Protocol: FAIL
2. Provide a Detailed Explanation to evidence the PASS or FAIL result
3. Provide the context From the Paper to evidence the PASS or FAIL result



**DOCUMENT CONTENT TO ANALYZE:**

=== DISCUSSION ===

This paper presents PREFER-IT, a set of co-created foundational requirements developed through a transdisciplinary workshop to provide an operational framework for realising inclusivity throughout the AI lifecycle and governance in healthcare. By engaging a diverse group of stakeholders in a structured co-creation process, we translated lived experiences and expert insights into actionable requirements for inclusive medical AI.
The workshop combined a preparatory PPI meeting and survey, a consensus-building exercise, and design thinking methods, including patient personas and journey mapping with the active involvement of patient experts. This structured process progressively moved from the broad challenge of inclusive medical AI to a set of concrete, experience-informed solutions and requirements, eventually culminating in the development of the PREFER-IT framework.
While existing frameworks often address fairness, trustworthiness or ethics in medical AI [2,9,18,25,[43][44][45][46], they generally remain conceptual or focus on technical dimensions. A unified, actionable framework for AI inclusivity, particularly within healthcare, remains absent, at least to our knowledge. PREFER-IT is a first step to respond to this gap by offering both a conceptual and applied foundation to guide the inclusive design, development, validation, implementation, monitoring, and governance of medical AI.
The co-creation process involved a small number of participants, providing an in-depth and explorative knowledge and exchange that generated valuable insights. The diversity of expertise and richness of perspectives provide a robust qualitative foundation for further empirical investigation. The co-created solutions, grounded in patient voices and lived experiences, particularly emphasised the human and governance dimensions of AI, reflecting the workshop's interdisciplinary composition across social sciences, ethics, and health policy.
However, the framework remains adaptable to technical domains and can guide integration at the data, process, and system levels of medical AI. While the discussions were primarily situated within a European context, this framing offers a strong basis for extending and validating the framework in other settings. A possible next step could be to test and adapt the framework across diverse contexts to capture a broader range of epistemic perspectives and societal concerns, ensuring its global relevance and applicability.
The workshop also revealed the importance of authentic participation. While patient involvement enriched the framework, it also highlighted risks of tokenism. Similar tensions have been observed in participatory AI design more broadly, where researchers and practitioners often struggle to reconcile participatory ambitions with practical constraints such as time, resources, and institutional norms [47]. To navigate these challenges, teams frequently rely on proxies (like stand-ins for stakeholders) that may inadvertently reinforce existing power asymmetries rather than dismantle them [47]. This underscores the need for co-creation to extend beyond isolated workshops toward sustained, reflexive engagement mechanisms that distribute decision-making authority and epistemic legitimacy. Without such safeguards, inclusion risks being symbolic rather than transformative.
Participants also identified several persistent gaps for realising inclusive medical AI. Rather than obstacles, these can be reframed as priority areas where PREFER-IT should be actively applied or advocated. For instance, the call for support tools to facilitate guideline adoption and compliance highlights the need for stronger public engagement in shaping and implementing AI regulation, as illustrated by the Algorithm Register of the Dutch government [48]. At the policy level, incorporating PREFER-IT principles into funding criteria, regulatory assessments, and institutional governance could help move inclusion from aspiration to routine practice. This approach would shape AI medical inclusivity in what is developed, how it is developed, who develops it, and how it is evaluated and refined over time.
Likewise, the demand for educational and capacity-building initiatives, such as patient training programs or education tools, aligns with ongoing policy efforts, such as those outlined in the AI Act [49], to support sustained implementation of PREFER-IT. Capacity-building should also target AI developers and healthcare professionals to bridge disciplinary gaps. As AI researchers often lack medical expertise, collaboration with clinicians and patients is crucial for understanding the clinical context and translating patient needs into technical design requirements. The more systemic concerns, including implementation complexity, safeguarding ethics, and balancing diverse values, highlight that PREFER-IT should not be approached as a simple checklist. Instead, it needs to be an adaptable infrastructure spanning the entire AI lifecycle and its various structural layers. The core challenge lies not in the absence of ethical discourse but in translating principles into actionable, context-sensitive mechanisms that can keep pace with technological innovation. Viewing inclusivity as a behavioural process that requires capability, opportunity, and motivation could strengthen the framework's practical applicability and promote organisational cultures that sustain inclusive AI development. [50] Looking ahead, the next steps involve translating PREFER-IT from a co-created framework into a living infrastructure for inclusive medical AI. This will require ongoing engagement with diverse stakeholders, including patients with intersectional vulnerabilities, regulators, and insurers. It also involves iterative testing in real-world projects, systematic evaluation across healthcare contexts, and continuous refinement based on user and patient feedback. Through this operationalisation, PREFER-IT aims to advance AI in healthcare that is not only effective but also equitable, ethical, legal, and socially responsible [51]. The co-creation process highlighted that realising inclusivity in medical AI requires more than just design features, depending on systemic enablers across multiple levels. In line with Wang and Blok's multi-level framework [25], PREFER-IT can be operationalised at the micro-level (individual AI issues), meso-level (organisational and systemic issues), and macro-level (philosophical issues). For example, policymakers can mandate transparency and equity audits; developers can integrate inclusion-by-design and participatory testing; researchers and clinicians can promote representative study designs; and patients and communities can codefine research priorities and evaluation metrics. The PREFER-IT framework could provide a basis for coordinating these actions across roles and governance levels. Finally, our research suggests a broader reframing: from an innovation-first logic to an inclusive-by-design paradigm. This shift positions inclusivity as a constitutive element of medical AI, embedded not only in outcomes but also in processes, governance structures, and power relations. By doing so, PREFER-IT helps address epistemic hierarchies in knowledge generation, challenging the dominance of quantitative and technical evidence and elevating the value of qualitative insights, experiential knowledge, and patient voices in shaping AI development. Through the development of PREFER-IT, we offer both a conceptual contribution and a practical resource to support stakeholders in realising inclusivity throughout the AI lifecycle in healthcare. The framework builds on, but also extends beyond, existing frameworks by positioning inclusion not as a peripheral ethical concern but as a core design principle, rooted in equity, stakeholder engagement, and governance accountability. Crucially, this process revealed that advancing inclusion in medical AI is not merely a matter of technological or design choices. It requires reconfiguring institutional logics, incentive structures, and policy environments. The PREFER-IT framework captures these multi-level dynamics and provides a framework for coordinated action. By explicitly incorporating the roles of patients and communities, it helps bridge the gap between AI development and implementation and the lived realities of those most affected by it. Ultimately, this process challenges dominant epistemic practices and paradigms in medical innovation, which often prioritise efficiency and technical advancement over equity and participation. Co-creation, as adopted here, is not merely a methodological technique but a stance that recognises the value of inclusion. It shifts the focus from innovation for to innovation with communities. 14,8% 18,5% 50,0% 20,0% 30,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 50,0% 25,0% 25,0% 1 Understanding the effects of bias on AI outputs, such as unfair recommendations. 27 15 3 9 0 10 2 3 5 0 4 4 0 0 0 5 5 0 0 0 8 4 0 4 0 55,6% 11,1% 33,3% 20,0% 30,0% 50,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 50,0% 0,0% 50,0% 2 Including diverse stakeholder voices throughout the AI lifecycle. 26 21 1 4 1 9 7 0 2 1 4 3 1 0 0 5 5 0 0 0 8 6 0 2 0 80,8% 3,8% 15,4% 77,8% 0,0% 22,2% 75,0% 25,0% 0,0% 100,0% 0,0% 0,0% 75,0% 0,0% 25,0% 3 Including the lived experiences of diverse groups in AI design and implementation. 27 22 0 5 0 10 8 0 2 0 4 4 0 0 0 5 5 0 0 0 8 5 0 3 0 81,5% 0,0% 18,5% 80,0% 0,0% 20,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 62,5% 0,0% 37,5% 4 Developing regulatory frameworks that address inclusivity in medical AI. 26 15 4 7 1 9 4 3 2 1 4 1 1 2 0 5 5 0 0 0 8 5 0 3 0 57,7% 15,4% 26,9% 44,4% 33,3% 22,2% 25,0% 25,0% 50,0% 100,0% 0,0% 0,0% 62,5% 0,0% 37,5% 5 Creating regulatory frameworks interpretable for all stakeholders. 27 10 9 8 0 10 0 6 4 0 4 0 2 2 0 5 5 0 0 0 8 5 1 2 0 37,0% 33,3% 29,6% 0,0% 60,0% 40,0% 0,0% 50,0% 50,0% 100,0% 0,0% 0,0% 62,5% 12,5% 25,0% 6 Ensuring training datasets represent diverse groups. 27 18 3 6 0 10 5 2 3 0 4 2 1 1 0 5 5 0 0 0 8 6 0 2 0 66,7% 11,1% 22,2% 50,0% 20,0% 30,0% 50,0% 25,0% 25,0% 100,0% 0,0% 0,0% 75,0% 0,0% 25,0% Assessing whether current data collection practices exacerbate inequalities.
27 20 3 4 0 10 5 3 2 0 4 4 0 0 0 5 5 0 0 0 8 6 0 2 0 74,1%
11,1% 14,8% 50,0% 30,0% 20,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 75,0% 0,0% 25,0% Investigating the societal impact of AI systems on diverse groups. 27 17 5 5 0 10 4 4 2 0 4 4 0 0 0 5 5 0 0 0 8 4 1 3 63,0% 18,5% 18,5% 40,0% 40,0% 20,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 50,0% 12,5% 37,5% 9 Continuous evaluation of AI across diverse groups. 26 13 4 9 1 9 4 3 2 1 4 1 1 2 0 5 5 0 0 0 8 3 0 5 50,0% 15,4% 34,6% 44,4% 33,3% 22,2% 25,0% 25,0% 50,0% 100,0% 0,0% 0,0% 37,5% 0,0% 62,5% 10 Identifying how medical AI affects human rights of diverse groups. 27 10 9 8 0 10 0 7 3 0 4 1 1 2 0 5 5 0 0 0 8 4 1 3 37,0% 33,3% 29,6% 0,0% 70,0% 30,0% 25,0% 25,0% 50,0% 100,0% 0,0% 0,0% 50,0% 12,5% 37,5% Identifying how medical AI affects public and personal values of diverse groups. 27 15 3 9 0 10 4 3 3 0 4 4 0 0 0 5 4 0 1 0 8 3 0 5 55,6% 11,1% 33,3% 40,0% 30,0% 30,0% 100,0% 0,0% 0,0% 80,0% 0,0% 20,0% 37,5% 0,0% 62,5% Incorporating AI advancements in healthcare context. 26 10 9 7 1 9 0 6 3 1 4 4 0 0 0 5 4 0 1 0 8 2 3 3 38,5% 34,6% 26,9% 0,0% 66,7% 33,3% 100,0% 0,0% 0,0% 80,0% 0,0% 20,0% 25,0% 37,5% 37,5% 13 Monitoring and detecting problems and risks in AI systems before their release. 27 17 4 6 0 10 4 3 3 0 4 3 0 1 0 5 4 1 0 0 8 6 0 2 63,0% 14,8% 22,2% 40,0% 30,0% 30,0% 75,0% 0,0% 25,0% 80,0% 20,0% 0,0% 75,0% 0,0% 25,0% 14 Collaborating between different scientific disciplines. 27 14 4 9 0 10 2 3 5 0 4 4 0 0 0 5 5 0 0 0 8 3 1 4 51,9% 14,8% 33,3% 20,0% 30,0% 50,0% 100,0% 0,0% 0,0% 100,0% 0,0% 0,0% 37,5% 12,5% 50,0% 15 Using a systems approach. 25 7 4 14 2 8 2 3 3 2 4 4 0 0 0 5 0 1 4 0 8 1 0 7 28,0% 16,0% 56,0% 25,0% 37,5% 37,5% 100,0% 0,0% 0,0% 0,0% 20,0% 80,0% 12,5% 0,0% 87,5% As a matter of principle, I don't think an AI tool should be used in the process of determining which topics are most pertinent. 25 4 16 5 2 9 1 6 2 1 4 0 3 1 0 4 1 3 0 1 8 2 4 2 16,0% 64,0% 20,0% 11,1% 66,7% 22,2% 0,0% 75,0% 25,0% 25,0% 75,0% 0,0% 25,0% 50,0% 25,0% Inventory and development of methods to include people in process of AI development and implementation 22 12 3 7 5 8 4 1 3 2 4 1 2 1 0 2 1 0 1 3 8 6 0 2 54,5% 13,6% 31,8% 50,0% 12,5% 37,5% 25,0% 50,0% 25,0% 50,0% 0,0% 50,0% 75,0% 0,0% 25,0% Adress AI inclusivity in different stages of the AI lifecycle 22 16 1 5 5 8 6 1 1 2 4 3 0 1 0 2 2 0 0 3 8 5 0 3 0 72,7% 4,5% 22,7% 75,0% 12,5% 12,5% 75,0% 0,0% 25,0% 100,0% 0,0% 0,0% 62,5% 0,0% 37,5% Adress AI inclusivity at different 'system' levels (micro, meso, macro) 22 16 1 5 5 8 6 1 1 2 4 3 0 1 0 2 1 0 1 3 8 6 0 2 0 72,7% 4,5% 22,7% 75,0% 12,5% 12,5% 75,0% 0,0% 25,0% 50,0% 0,0% 50,0% 75,0% 0,0% 25,0% Develop methods/frameworks to promote inclusivity for medical experts working with AI to increase safe adoption (e.g. AI literacy) 20 12 4 4 7 8 5 1 2 2 4 1 2 1 0 2 1 0 1 3 6 5 1 0 2 60,0% 20,0% 20,0% 62,5% 12,5% 25,0% 25,0% 50,0% 25,0% 50,0% 0,0% 50,0% 83,3% 16,7% 0,0% Make sure AI is at the service of patients 19 9 3 7 8 7 3 2 2 3 4 3 0 1 0 2 2 0 0 3 6 1 1 4 2 47,4% 15,8% 36,8% 42,9% 28,6% 28,6% 75,0% 0,0% 25,0% 100,0% 0,0% 0,0% 16,7% 16,7% 66,7% When developing and deploying healthcare AI, patients' rights should be taken into account. 6 4 1 1 21 1 0 1 0 9 0 0 0 0 4 1 1 0 0 4 4 3 0 1 4 66,7% 16,7% 16,7% 0,0% 100,0% 0,0% NA NA NA 100,0% 0,0% 0,0% 75,0% 0,0% 25,0%
Legend: The statements highlighted in grey represent those with the highest levels of agreement (defined as having over 66% of participants selecting "Agree" across the entire sample). Participants were asked to vote on the statements, considering what they felt were the most significant challenges we should focus on during the five-day in-person workshop. This means that when participants choose "Disagree" or "Pass", it is not necessarily because they disagree or are unsure about the statement, but because they felt it was not as big a priority to focus on the workshop as other statements. Among the 27 participants, four distinct opinion groups emerged in Pol.is. These groups were defined by two criteria: first, members within each group exhibited similar voting patterns across multiple statements; second, their voting behaviour was consistently different from that of participants in the other groups. Pol.is uses the k-means clustering algorithm to group participants into clusters and the silhouette coefficient to select an optimal number of clusters.
•
Participatory and co-design approaches • Representative and diverse data • Education and digital literacy • Fairness • Ethical and legal accountability • Real-world validation and feedback • Inclusive communication • Technical interoperability
Fig 1
Fig 1 describes the framework for the different stages of the workshop, based on the design
Fig 1 .
Fig 1. Structured workflow for the workshop. Adaptation of the double diamond model
survey responses, along with the initial 16 statements (two per theme) and the seven submitted anonymously by participants, drafted to inform the Pol.is deliberation. a New statements added by the participants. Participants of the workshop were asked to vote, through Pol.is, on each statement based on what they considered the most pressing challenges to address during the five-day in-person workshop (Insights stage).
1 . 6 .
Distinctive, addressing unique and complementary aspects of the workshop's overarching goal; 2. Balanced between human and technical dimensions, grounded in patient needs and lived experiences; 3. Interdisciplinary, suitable for engagement across diverse fields of expertise; 4. Aligned with the working definition of inclusive medical AI, reflecting the core pillars of ability, opportunity, and dignity for equitable health outcomes; 5. Actionable and impactful, enabling actionable insights and identifying research gaps; and Reflective of the PPI perspective, consistent with priorities identified by the PPI focus group. Three challenges with each a corresponding research question were then identified to guide the work and discussions of the participants during the in-person workshop (Insights stage): A. Integrating lived experiences and stakeholder voices across the AI lifecycle: How can we ensure that the lived experiences of diverse groups and stakeholder voices are systematically included throughout the AI lifecycle? B. Designing data collection practices to promote fairness and prevent inequalities: How can we ensure that data collection practices are representative, fair, and equitable and do not exacerbate inequalities while minimising biases in AI systems? C. Fostering regulatory frameworks to uphold human rights and promote inclusivity: How can we ensure that regulatory and ethical frameworks are used in AI development and implementation to safeguard human rights, address ethical concerns, promote trust, and support inclusive practices across the AI lifecycle?
process culminated in a final phase of the PREFER-IT framework consolidation based on the thematic analysis and clustering of the workshop data. The collective insights and results from the in-person workshop are summarised in Fig 2.
Fig 2 .
Fig 2. Results of the workshop summarised by day (D). The collective insights and main
Fig 3 .
Fig 3. PREFER-IT framework. How PREFER-IT originated from the thematic clusters that
Fig 4 each component of the PREFER-IT framework aligns with specific structural layers and phases of the medical AI lifecycle. For instance, Participatory and co-design approaches (P) are essential throughout all stages of the AI lifecycle and must actively involve all stakeholders (what is labelled as "humans"). Representative and diverse data (R) plays a crucial role within the data layer, particularly during the design phase. Elements such as Education and digital literacy (E), Ethical and legal accountability (E), and Inclusive communication (I) are fundamental governance considerations that span the entire AI lifecycle. Meanwhile, Fairness (F) and Technical interoperability (T) are core to the process layer and should be prioritised during development and validation. Finally, Real-world validation and feedback (R) are vital in any AI system, ensuring its adaptation to evolving socio-technical contexts, and should be considered during the validation, implementation and monitoring stages.
Fig 4 .
Fig 4. Implementation of the PREFER-IT framework across structural layers and the AI
Fig 5 .
Fig 5. Operationalising PREFER-IT. Mapping of the prototype solutions co-created during


=== INTRODUCTION ===

The advances and applications of artificial intelligence (AI) in healthcare, referred to in this paper as medical AI, have the potential to transform society. Medical AI could improve diagnosis, personalise treatment, increase operational efficiency, optimise resources such as staff time, equipment, and hospital capacity, and improve patients' experience [1][2][3][4][5]. Despite these possible benefits, medical AI carries known risks: it may reinforce existing biases against historically disadvantaged populations, underserved groups, and individuals experiencing (multiple) layers of vulnerability [6], and it remains prone to errors and patient harm [7][8][9]. If left unaddressed, these risks could ultimately exacerbate health disparities. [1]. Furthermore, medical AI raises questions regarding accessibility, autonomy, equity, human rights, and inclusivity [1]. Inclusivity is a recognised principle in healthcare, but concerns remain that AI may reproduce or exacerbate disparities across patient and population groups [7,10]. Groups such as older adults, ethnic minorities, and individuals with disabilities may face disproportionate risks or limited benefits from AI innovations [1,[10][11][12].
The literature shows a growing attention to issues of bias, trust, and fairness in AI [2,3,8,9,11,[13][14][15][16][17][18][19][20][21][22], with different disciplines addressing these issues from complementary perspectives. Medical informatics research prioritises algorithmic fairness metrics [7,9] and technical bias mitigation [16,23,24], focusing on the iterative phases of the AI lifecycle, which encompass the design, development, validation, implementation, and monitoring of AI [2], while often not addressing social determinants of health and inequity. Ethics, legal, and social science literature emphasise autonomy, justice, and human rights, frequently lacking operationalisation into technical or clinical practice [25][26][27]. Clinical and public health research addresses data representativeness, patient outcomes, and health disparities, often concentrated in high-income contexts [7,10,26].
While each field contributes valuable insights to these issues, these contributions have largely evolved in parallel, resulting in a fragmented landscape of efforts that, while individually valuable, collectively limit the development of cohesive and actionable solutions. This fragmentation results in uneven attention to people experiencing (multiple) layers of vulnerabilities [6] (e.g., more emphasis on race and gender bias than on age, disability or their intersections [12,28]) and gaps across the AI lifecycle, with deployment, monitoring, and governance being less explored than data collection and model design, for example [26].
Moreover, current approaches rarely engage with broader notions of inclusion, such as the social and structural contexts in which AI tools operate [12,29] or the systemic, participatory integration of marginalised voices and needs across the AI lifecycle.
To build on these diverse contributions, this paper adopts the lens of inclusive medical AI to examine how principles of inclusion can be systematically embedded into the AI lifecycle and governance of AI in healthcare.
We define medical inclusivity as the equal ability, opportunity and right of all individuals, particularly those who are underserved, vulnerable or otherwise disadvantaged based on identity and/or circumstances, to access health services, receive compassionate and highquality care, and achieve equitable health outcomes, in ways that respect human dignity. [13].
As a prerequisite for medical inclusivity, AI systems must be designed and implemented to ensure equitable access and use across all population groups, regardless of attributes such as age, sex, gender, income, race, ethnicity, sexual orientation, disability, or other characteristics protected by human rights principles [1]. Some works, although not specifically focused on healthcare, have attempted to address this gap by exploring diversity and inclusion by design [9,24,25] or by advocating for stakeholder engagement in design and evaluation [13,30]. For example, Zowghi and da Rimini propose five pillars (humans, data, process, system, governance) for embedding diversity and inclusion by design across AI ecosystems. This approach extends beyond fairness metrics by urging the inclusion of diverse voices, contexts, and institutions [30]. Wang and Blok propose a multilevel framework that shifts from AI micro-level issues (e.g., dataset bias, algorithm transparency) to structural concerns, from meso (clinical and organisational) to macro (systemic and socio-political) levels, which shape AI's broader impacts [25].
The FUTURE-AI guideline represents a first step, offering a structured, global consensus on trustworthy AI in healthcare across the AI lifecycle. While it addresses fairness, universality, and stakeholder engagement, its implementation of inclusivity remains limited: inclusivity is mainly viewed through bias mitigation and technical fairness metrics, rather than as a systematic design principle rooted in different contexts, lived experiences, and societal perspectives. [2] For healthcare specifically, the field remains fragmented, where valuable yet dispersed insights still need to be synthesised into a comprehensive, pragmatic framework for inclusive medical AI. While technical and governance-oriented guidelines exist [2], and ethical principles have been articulated in policy documents [1,11], systematic approaches to realise inclusivity remain dispersed and underdeveloped. General frameworks on inclusivity are not always transferable to healthcare, given the high-stakes and sensitive nature of this domain [27,32]. Medical AI tools have a direct impact on health outcomes, and the risks are substantial. Furthermore, healthcare is characterised by hierarchies and power dynamics, knowledge asymmetries between experts and patients, and the influence of professional and institutional norms [33][34][35]. These conditions can lead to the exclusion of voices and lived experiences, without explicit mechanisms for medical inclusivity.
This paper bridges this gap by presenting a set of co-created foundational requirements, named PREFER-IT, for the design, development, validation, implementation, monitoring, and governance of inclusive medical AI. Developed through a transdisciplinary co-creation process involving multiple disciplines and stakeholder perspectives, including patients, PREFER-IT provides a practical framework to realise medical AI inclusivity. In this paper, we describe how PREFER-IT was developed.


=== SUPPLEMENTARY ===

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


=== ABSTRACT ===
Artificial intelligence (AI) in healthcare holds transformative potential but risks exacerbating existing health disparities if inclusivity is not explicitly accounted for. This study addresses the disconnected discussions on inclusive medical AI by developing a comprehensive framework, PREFER-IT. This framework is based on the outcomes of a five-day transdisciplinary cocreation workshop that involved 37 experts from diverse backgrounds, including healthcare, ethics, law, social sciences, AI, and patient advocacy. For this workshop, we used design thinking and participatory methodologies to develop a framework for realising inclusive medical AI. We identified three key challenges for realising inclusive medical AI: integrating the lived experiences and stakeholder voices across the AI lifecycle, designing data collection practices that promote fairness and prevent inequalities, and fostering regulatory frameworks to uphold human rights and promote inclusivity. The analysis of participants' perspectives informed the development of eight key thematic clusters of PREFER-IT: Participatory and codesign approaches (P), Representative and diverse data (R), Education and digital literacy (E), Fairness (F), Ethical and legal accountability (E), Real-world validation and feedback (R), Inclusive communication (I), and Technical interoperability (T). These elements were mapped across structural layers of AI (humans, data, system, process, and governance) and the AI lifecycle to guide inclusive design, development, validation, implementation, monitoring, and governance. This framework fosters stakeholder engagement and systemic change, positioning inclusion as a guiding principle in practice. PREFER-IT offers a practical and conceptual contribution for how to include ethical, legal and societal aspects when aiming to foster responsible and inclusive AI in healthcare.


=== KEYWORDS ===
PPF|| SSL|| WB|| SdK|| CL|| LB|| MP||Data Curation: PPF|| SSL|| WB|| LB||Investigation: PPF|| SSL|| WB|| JG|| LB|| MP||Methodology: PPF|| SSL|| WB|| JG|| LB|| MP||Supervision: SdK|| CL|| MP||Visualisation: PPF||Writing -Original Draft Preparation: PPF||Writing -Review & Editing: all the authors


=== TITLE ===
PREFER-IT: A transdisciplinary co-created framework to realise inclusive medical AI

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents original data collected during a five‑day transdisciplinary co‑creation workshop (37 experts, 27 participants, survey responses, voting data, and clustering results). These data are newly generated for this study and are presented in figures and tables (e.g., Fig 1–5, workshop voting statistics). The paper does not describe a structured clinical study protocol. Therefore, it satisfies the medRxiv requirement of containing new data.  
**CONTEXT:**  
- “This paper presents PREFER‑IT, a set of co‑created foundational requirements developed through a transdisciplinary workshop …”  
- “The workshop combined a preparatory PPI meeting and survey, a consensus‑building exercise, and design thinking methods, including patient personas and journey mapping …”  
- “Participants of the workshop were asked to vote, through Pol.is, on each statement …”  
- “The collective insights and results from the in‑person workshop are summarised in Fig 2.”  
- “Fig 1 describes the framework for the different stages of the workshop …”  
- “Fig 2. Results of the workshop summarised by day (D).”  
These excerpts demonstrate that the manuscript contains newly collected workshop data, qualifying it as a submission with new data.

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents original data collected during a five‑day transdisciplinary co‑creation workshop (e.g., participant numbers, survey responses, Pol.is voting results, and figures showing percentage distributions). These data are not reused from prior publications and constitute new empirical findings, satisfying the medRxiv requirement for new data. The paper does not describe a structured clinical study protocol.  
**CONTEXT:**  
- “This paper presents PREFER‑IT, a set of co‑created foundational requirements developed through a transdisciplinary workshop…”  
- “The workshop combined a preparatory PPI meeting and survey, a consensus‑building exercise, and design thinking methods…”  
- “Participants of the workshop were asked to vote, through Pol.is, on each statement…”  
- Figures 1–5 and accompanying tables display workshop results (e.g., “14,8% 18,5% 50,0% 20,0% 30,0% 100,0% …”, “Fig 2. Results of the workshop summarised by day (D).”)

=== ABSTRACT ===
Interventions to 6 long COVID patients were monitored and compared with the SARS CoV 2 antibody spectrum for 15 variants for both quantity and quality (avidity):


=== TITLE ===
Long COVID Pathophysiology Interventions and Recovery Case Studies profiled with SARS-CoV-2 Spike Protein Antibody Variant Spectrum


=== DISCUSSION ===

The alignment of the COVID multivariant antibody spectrum with the onset of symptoms provides an insight into targeting for any interventions. A hole in the antibody spectrum in either quantity or quality suggests a non-clearing serum and the risk of persistent virus. The alignment of the spectrum and onset of symptoms increases the probability of identifying which variant is responsible, notwithstanding the mutation of a unique variant during an infection. Monoclonal antibodies effective against the persistent variant are a potential therapy to plug the gap in the antibody spectrum and initiate clearance; patients A-D. Patient A is the most extensive investigation exploring both the effects of serum concentration on perfusion into body compartments, including accessing CSF, and subsequent repair of the antibody spectrum. Evusheld is well targeted at the Wuhan variant with good concentrations and high quality, Figure 2(c) and (d), leading to perfusion. The unique observation is the presence of Evusheld in the CSF, Figure 3, made possible because of the 'fingerprint' derived from the antibody spectrum. The estimated monoclonal antibody perfusion 7 into the brain of 0.351% and the observed percentage at 0.5% is striking. Perfusion into the CSF correlates with reduction of migraine frequence from daily to monthly after the second dose, and subsequent return to near-normal brain function. The patient recovery from bed-bound to approximately 80% normal function details a long journey with a number of other interventions including apheresis, further signifying the complexity and personalisation needed in the recovery journey.
The Evusheld intervention of Patient B, Figure 5, is mistargeted against the gap in the antibody spectrum with persistent Omicron BA.1/5 against which Evusheld has no efficacy; Sipavibart would have been a better drug choice as seen in the therapy of Patient D, Figure 7. However, Sipavibart is well targeted for the poor antibody quality to Wuhan in Patient D, with a dosing regimen consistent with the perfusion requirements. Sipavibart has significant antibody quality up to variants KP.3, with some gaps, Figure 7(f), although binding concentrations are low for variants after BA.1. The perfusion regimen for Sipavibart against later variants needs consideration. The dominant symptom for Patient D is dysautonomia suggesting a requirement for perfusion into the CSF. Early in the intervention, there is a significant improvement in the HRV response but 8 weeks post infusion there has been no significant improvement in symptoms. Both Sipavibart and Evusheld have had modifications to the antibody, reducing the FcγR receptor binding and mutations in the C1q binding site to improve the half-life in the blood. However, these antibody-dependent cellular cytotoxicity (ADCC) modifications 15 are required for the clearance of an antibody-virus complex.
Unlike the Patients A, B, and D who show hypo-immune or immuno-compromised antibody spectra, the Pemgarda intervention for Patient C, Figure 6, would not be indicated for viral persistence as they clearly show a hyperimmune response. The antibody concentrations were measured at 176 mg/L prior to intervention, one of the highest levels observed and potentially close to a maximum antibody production rate for the patient. The hyperimmune response suggests some autoimmune reaction to a natural body protein or ongoing response to the production of spike protein. The Pemgarda infusion increased the concentration nearly 10fold to 1189 mg/L, correlating with almost complete relief from all symptoms within one month. The mechanisms of action consistent with the relief are persistent spike protein production which is fully complexed and removed by the presence of the Pemgarda or downregulation of autoantibody production. Either would suggest the symptom relief would depend on the concentration of Pemgarda which has a half-life of 49 days 16 . Antibody concentration will fall to the pre-intervention levels in 2.67 half-lives, or 131 days, with the potential return of symptoms.


=== INTRODUCTION ===

Long COVID is a general term for a syndrome of more than 250 symptoms that occur as post-acute sequalae to SARS-CoV-2 (PASC) exposure and potentially post-vaccination. The WHO has standardised the definition of long COVID 1 based on the persistence of symptoms 2 more than 20 weeks after confirmed infection, and if there are no other clear causes. The definition is, in part, confounded by asymptomatic infection. There are currently no objective diagnostic tests. Viral particles and mRNA have been identified in a large number of tissue and organ biopsies 3 as evidence of persistent virus. Further, circulating spike protein has been detected in the serum of 60% of long COVID patients in some studies 4 , pointing to viral reactivation mechansims 5 or to more generally mobile virus. Interventions have been tried, targeting active virus with re-purposed or new antiviral drugs and/or monoclonal antibodies to remove both spike and latent or active virus 6 . However, none of the trials have been successful, with significant difficulties in defining primary and secondary outcomes based on symptom improvement alone.
The effectiveness of antibody clearance of latent virus depends on the accurate targeting of the antibody to the persistent variant and the accessibility of the latent viral reservoirs to the antibody infusion. Tissue infusion based on plasma concentration of mAbs 7 has estimated perfusion into parts of the body as percentages of plasma concentration: lung 14.9%, heart 10.2%, kidney 13.7%, muscle 3.97%, skin 15.7%, small intestine 5.22%, large intestine 5.03%, spleen 12.8%, liver 12.1%, bone 7.27%, stomach 4.98%, lymph node 8.46%, adipose 4.78%, brain 0.351%, pancreas 6.4%, testes 5.88%, thyroid 67.5%, and thymus 6.62%. Latent viral reservoirs in these locations could account for many of the symptoms of long COVID, especially brain fog. Intramuscular and intravenous interventions provide different perfusion profiles in the patient and could influence attainment of critical antibody concentrations for viral clearance. Even with favourable perfusion, symptoms may also be slow to recover, if at all. The two most frequently proposed pathophysiologies for long COVID are viral persistence and autoimmunity [8][9][10] , both of which would likely have signatures in the antibody immunity profile of patients. Persistent virus may be explained by a cellular-based recovery from acute infection but poor antibody production to affect complete clearance of the virus from the body. The antibody response of the patient is variant specific [11][12][13] , producing antibodies following natural infection and immunisation, leading to a variant antibody spectrum in concentration as in Figure 1, which shows antibody profiles to 15 SARS-CoV-2 variants. The new antibody quality measure [11][12][13] reflects the antibody binding to the antigen, resulting in a complex that has a half-life of longer than 2 hours. It is thus a measurement of the antibody avidity in the complex.
A patient may be given a hypoimmune or immuno-compromised classification, Figure 1(a), if it shows a gap in the antibody spectrum in either quantity or quality (or both) that coincides with the onset of long COVID symptoms. The classification allows the likely persistent variant to be identified and so a targeted monoclonal antibody or vaccine therapy response. Similarly, elevated antibody levels in the spectrum following vaccination or infection and associated with the onset of long COVID symptoms, Figure 1(b), would indicate a hyperimmune or autoimmune response. Here, the intervention pathway is less clear depending on the mechanism of hyperimmunity.
Figure 1 The association of the prevalent variant wave and the antibody immunity spectrum in immunocompromised and hyperimmune patients: (a) A poor antibody spectrum of a long COVID patient can be aligned with Variant waves and compared with the onset of long COVID symptoms. The antibody spectrum shows low quantity and quality of antibodies associated with the omicron infection triggering the long COVID symptoms and leads to an assignment of the persistent variant; and (b) the onset of symptoms associated with a vaccination, with the antibody immunity concentration and avidity leading to an assignment of variant spike production based on vaccine variant target or autoimmunity following infection.
In this paper, interventional case studies are reviewed against the initial classification of the patients using a SARS-CoV-2 antibody spectrum test in either quantity or quality, the onset of symptoms, and likely exposure to the variant. Interventions to fill the endotype gaps in the antibody spectrum with monoclonal antibodies were considered as well as protein vaccine repair. In addition, a natural second infection leading to recovery is also characterised by changes in antibody spectra. Further, a hyperimmune intervention using a monoclonal antibody is also characterised. The change in symptoms is considered for each patient case. The heterogeneity of the long COVID patient cohort is considered for antibody immunity profiles, latent virus variant reservoirs, spike protein production, autoimmunity, and intervention success measures.

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3. Check for presence of standard sections:
 - Abstract, Introduction, Methods, Results, Discussion/Conclusion.
 - Accept variants such as Background, Materials and Methods, Findings, or Conclusions.
4. Look for language suggesting original research or systematic methodology, such as:
 - “we conducted experiments,” “our findings show,” “we systematically reviewed,” “studies were selected based on.”
5. Mark the manuscript as FAIL if it lacks structural and methodological indicators of original research or systematic review.
- Narrative summaries, essays, or theory pieces without structured methodology should be marked FAIL.
6.  If it IS a research paper or a systematic review, mark as PASS. If it is NOT a research paper or a systematic review, mark as FAIL.

Output Format
- PASS / FAIL
- Detailed Explanation
- Context From the Paper

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

**SPECIFIC CHECK:**
Objective  
Confirm whether the manuscript qualifies as a research paper or a systematic review based on structural and methodological characteristics.

Instructions  
1. Evaluate the manuscript for the following:
 - A research paper presents original research findings based on experiments, observations, or analyses conducted by the authors.
 - A systematic review provides a structured review of prior research following a clear methodology (e.g., inclusion/exclusion criteria, data synthesis).
2. For systematic reviews, look for indicators such as:
 - Mention of search strategies, search terms, or databases used.
 - Use of established frameworks like PRISMA, PICO, or equivalent (optional but useful indicators).
3. Check for presence of standard sections:
 - Abstract, Introduction, Methods, Results, Discussion/Conclusion.
 - Accept variants such as Background, Materials and Methods, Findings, or Conclusions.
4. Look for language suggesting original research or systematic methodology, such as:
 - “we conducted experiments,” “our findings show,” “we systematically reviewed,” “studies were selected based on.”
5. Mark the manuscript as FAIL if it lacks structural and methodological indicators of original research or systematic review.
- Narrative summaries, essays, or theory pieces without structured methodology should be marked FAIL.
6.  If it IS a research paper or a systematic review, mark as PASS. If it is NOT a research paper or a systematic review, mark as FAIL.

Output Format
- PASS / FAIL
- Detailed Explanation
- Context From the Paper

**DOCUMENT CONTENT TO ANALYZE:**

=== ABSTRACT ===
Interventions to 6 long COVID patients were monitored and compared with the SARS CoV 2 antibody spectrum for 15 variants for both quantity and quality (avidity):


=== TITLE ===
Long COVID Pathophysiology Interventions and Recovery Case Studies profiled with SARS-CoV-2 Spike Protein Antibody Variant Spectrum


=== DISCUSSION ===

The alignment of the COVID multivariant antibody spectrum with the onset of symptoms provides an insight into targeting for any interventions. A hole in the antibody spectrum in either quantity or quality suggests a non-clearing serum and the risk of persistent virus. The alignment of the spectrum and onset of symptoms increases the probability of identifying which variant is responsible, notwithstanding the mutation of a unique variant during an infection. Monoclonal antibodies effective against the persistent variant are a potential therapy to plug the gap in the antibody spectrum and initiate clearance; patients A-D. Patient A is the most extensive investigation exploring both the effects of serum concentration on perfusion into body compartments, including accessing CSF, and subsequent repair of the antibody spectrum. Evusheld is well targeted at the Wuhan variant with good concentrations and high quality, Figure 2(c) and (d), leading to perfusion. The unique observation is the presence of Evusheld in the CSF, Figure 3, made possible because of the 'fingerprint' derived from the antibody spectrum. The estimated monoclonal antibody perfusion 7 into the brain of 0.351% and the observed percentage at 0.5% is striking. Perfusion into the CSF correlates with reduction of migraine frequence from daily to monthly after the second dose, and subsequent return to near-normal brain function. The patient recovery from bed-bound to approximately 80% normal function details a long journey with a number of other interventions including apheresis, further signifying the complexity and personalisation needed in the recovery journey.
The Evusheld intervention of Patient B, Figure 5, is mistargeted against the gap in the antibody spectrum with persistent Omicron BA.1/5 against which Evusheld has no efficacy; Sipavibart would have been a better drug choice as seen in the therapy of Patient D, Figure 7. However, Sipavibart is well targeted for the poor antibody quality to Wuhan in Patient D, with a dosing regimen consistent with the perfusion requirements. Sipavibart has significant antibody quality up to variants KP.3, with some gaps, Figure 7(f), although binding concentrations are low for variants after BA.1. The perfusion regimen for Sipavibart against later variants needs consideration. The dominant symptom for Patient D is dysautonomia suggesting a requirement for perfusion into the CSF. Early in the intervention, there is a significant improvement in the HRV response but 8 weeks post infusion there has been no significant improvement in symptoms. Both Sipavibart and Evusheld have had modifications to the antibody, reducing the FcγR receptor binding and mutations in the C1q binding site to improve the half-life in the blood. However, these antibody-dependent cellular cytotoxicity (ADCC) modifications 15 are required for the clearance of an antibody-virus complex.
Unlike the Patients A, B, and D who show hypo-immune or immuno-compromised antibody spectra, the Pemgarda intervention for Patient C, Figure 6, would not be indicated for viral persistence as they clearly show a hyperimmune response. The antibody concentrations were measured at 176 mg/L prior to intervention, one of the highest levels observed and potentially close to a maximum antibody production rate for the patient. The hyperimmune response suggests some autoimmune reaction to a natural body protein or ongoing response to the production of spike protein. The Pemgarda infusion increased the concentration nearly 10fold to 1189 mg/L, correlating with almost complete relief from all symptoms within one month. The mechanisms of action consistent with the relief are persistent spike protein production which is fully complexed and removed by the presence of the Pemgarda or downregulation of autoantibody production. Either would suggest the symptom relief would depend on the concentration of Pemgarda which has a half-life of 49 days 16 . Antibody concentration will fall to the pre-intervention levels in 2.67 half-lives, or 131 days, with the potential return of symptoms.


=== INTRODUCTION ===

Long COVID is a general term for a syndrome of more than 250 symptoms that occur as post-acute sequalae to SARS-CoV-2 (PASC) exposure and potentially post-vaccination. The WHO has standardised the definition of long COVID 1 based on the persistence of symptoms 2 more than 20 weeks after confirmed infection, and if there are no other clear causes. The definition is, in part, confounded by asymptomatic infection. There are currently no objective diagnostic tests. Viral particles and mRNA have been identified in a large number of tissue and organ biopsies 3 as evidence of persistent virus. Further, circulating spike protein has been detected in the serum of 60% of long COVID patients in some studies 4 , pointing to viral reactivation mechansims 5 or to more generally mobile virus. Interventions have been tried, targeting active virus with re-purposed or new antiviral drugs and/or monoclonal antibodies to remove both spike and latent or active virus 6 . However, none of the trials have been successful, with significant difficulties in defining primary and secondary outcomes based on symptom improvement alone.
The effectiveness of antibody clearance of latent virus depends on the accurate targeting of the antibody to the persistent variant and the accessibility of the latent viral reservoirs to the antibody infusion. Tissue infusion based on plasma concentration of mAbs 7 has estimated perfusion into parts of the body as percentages of plasma concentration: lung 14.9%, heart 10.2%, kidney 13.7%, muscle 3.97%, skin 15.7%, small intestine 5.22%, large intestine 5.03%, spleen 12.8%, liver 12.1%, bone 7.27%, stomach 4.98%, lymph node 8.46%, adipose 4.78%, brain 0.351%, pancreas 6.4%, testes 5.88%, thyroid 67.5%, and thymus 6.62%. Latent viral reservoirs in these locations could account for many of the symptoms of long COVID, especially brain fog. Intramuscular and intravenous interventions provide different perfusion profiles in the patient and could influence attainment of critical antibody concentrations for viral clearance. Even with favourable perfusion, symptoms may also be slow to recover, if at all. The two most frequently proposed pathophysiologies for long COVID are viral persistence and autoimmunity [8][9][10] , both of which would likely have signatures in the antibody immunity profile of patients. Persistent virus may be explained by a cellular-based recovery from acute infection but poor antibody production to affect complete clearance of the virus from the body. The antibody response of the patient is variant specific [11][12][13] , producing antibodies following natural infection and immunisation, leading to a variant antibody spectrum in concentration as in Figure 1, which shows antibody profiles to 15 SARS-CoV-2 variants. The new antibody quality measure [11][12][13] reflects the antibody binding to the antigen, resulting in a complex that has a half-life of longer than 2 hours. It is thus a measurement of the antibody avidity in the complex.
A patient may be given a hypoimmune or immuno-compromised classification, Figure 1(a), if it shows a gap in the antibody spectrum in either quantity or quality (or both) that coincides with the onset of long COVID symptoms. The classification allows the likely persistent variant to be identified and so a targeted monoclonal antibody or vaccine therapy response. Similarly, elevated antibody levels in the spectrum following vaccination or infection and associated with the onset of long COVID symptoms, Figure 1(b), would indicate a hyperimmune or autoimmune response. Here, the intervention pathway is less clear depending on the mechanism of hyperimmunity.
Figure 1 The association of the prevalent variant wave and the antibody immunity spectrum in immunocompromised and hyperimmune patients: (a) A poor antibody spectrum of a long COVID patient can be aligned with Variant waves and compared with the onset of long COVID symptoms. The antibody spectrum shows low quantity and quality of antibodies associated with the omicron infection triggering the long COVID symptoms and leads to an assignment of the persistent variant; and (b) the onset of symptoms associated with a vaccination, with the antibody immunity concentration and avidity leading to an assignment of variant spike production based on vaccine variant target or autoimmunity following infection.
In this paper, interventional case studies are reviewed against the initial classification of the patients using a SARS-CoV-2 antibody spectrum test in either quantity or quality, the onset of symptoms, and likely exposure to the variant. Interventions to fill the endotype gaps in the antibody spectrum with monoclonal antibodies were considered as well as protein vaccine repair. In addition, a natural second infection leading to recovery is also characterised by changes in antibody spectra. Further, a hyperimmune intervention using a monoclonal antibody is also characterised. The change in symptoms is considered for each patient case. The heterogeneity of the long COVID patient cohort is considered for antibody immunity profiles, latent virus variant reservoirs, spike protein production, autoimmunity, and intervention success measures.

**RESULT:** PASS  
**EXPLANATION:** The manuscript reports original data from six long‑COVID patients, describing how antibody spectra were measured and how specific monoclonal‑antibody interventions were applied and monitored. The structure includes an abstract, introduction, discussion, and detailed patient case descriptions, all of which indicate primary research rather than a systematic review.  
**CONTEXT:**  
- “Interventions to 6 long COVID patients were monitored and compared with the SARS CoV 2 antibody spectrum for 15 variants for both quantity and quality (avidity):” (Abstract)  
- “In this paper, interventional case studies are reviewed against the initial classification of the patients using a SARS‑CoV‑2 antibody spectrum test in either quantity or quality, the onset of symptoms, and likely exposure to the variant.” (Introduction)  
- “Patient A is the most extensive investigation exploring both the effects of serum concentration on perfusion into body compartments, including accessing CSF, and subsequent repair of the antibody spectrum.” (Discussion)

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents original case‑study data on four long‑COVID patients, including antibody spectrum measurements, intervention details, and observed outcomes. It contains the typical structure of a research paper (Abstract, Introduction, Discussion) and uses language indicating primary data collection (“interventions to 6 long COVID patients were monitored…”, “patient recovery from bed‑bound…”) rather than a systematic review methodology. No search strategy or review protocol is described, confirming it is not a systematic review.  
**CONTEXT:**  
- Abstract: “Interventions to 6 long COVID patients were monitored and compared with the SARS CoV 2 antibody spectrum…”  
- Discussion: “The alignment of the COVID multivariant antibody spectrum with the onset of symptoms provides an insight into targeting for any interventions.”  
- Introduction: “In this paper, interventional case studies are reviewed against the initial classification of the patients using a SARS‑CoV‑2 antibody spectrum test…”  
These excerpts demonstrate original research rather than a systematic review.

=== DISCUSSION ===

The alignment of the COVID multivariant antibody spectrum with the onset of symptoms provides an insight into targeting for any interventions. A hole in the antibody spectrum in either quantity or quality suggests a non-clearing serum and the risk of persistent virus. The alignment of the spectrum and onset of symptoms increases the probability of identifying which variant is responsible, notwithstanding the mutation of a unique variant during an infection. Monoclonal antibodies effective against the persistent variant are a potential therapy to plug the gap in the antibody spectrum and initiate clearance; patients A-D. Patient A is the most extensive investigation exploring both the effects of serum concentration on perfusion into body compartments, including accessing CSF, and subsequent repair of the antibody spectrum. Evusheld is well targeted at the Wuhan variant with good concentrations and high quality, Figure 2(c) and (d), leading to perfusion. The unique observation is the presence of Evusheld in the CSF, Figure 3, made possible because of the 'fingerprint' derived from the antibody spectrum. The estimated monoclonal antibody perfusion 7 into the brain of 0.351% and the observed percentage at 0.5% is striking. Perfusion into the CSF correlates with reduction of migraine frequence from daily to monthly after the second dose, and subsequent return to near-normal brain function. The patient recovery from bed-bound to approximately 80% normal function details a long journey with a number of other interventions including apheresis, further signifying the complexity and personalisation needed in the recovery journey.
The Evusheld intervention of Patient B, Figure 5, is mistargeted against the gap in the antibody spectrum with persistent Omicron BA.1/5 against which Evusheld has no efficacy; Sipavibart would have been a better drug choice as seen in the therapy of Patient D, Figure 7. However, Sipavibart is well targeted for the poor antibody quality to Wuhan in Patient D, with a dosing regimen consistent with the perfusion requirements. Sipavibart has significant antibody quality up to variants KP.3, with some gaps, Figure 7(f), although binding concentrations are low for variants after BA.1. The perfusion regimen for Sipavibart against later variants needs consideration. The dominant symptom for Patient D is dysautonomia suggesting a requirement for perfusion into the CSF. Early in the intervention, there is a significant improvement in the HRV response but 8 weeks post infusion there has been no significant improvement in symptoms. Both Sipavibart and Evusheld have had modifications to the antibody, reducing the FcγR receptor binding and mutations in the C1q binding site to improve the half-life in the blood. However, these antibody-dependent cellular cytotoxicity (ADCC) modifications 15 are required for the clearance of an antibody-virus complex.
Unlike the Patients A, B, and D who show hypo-immune or immuno-compromised antibody spectra, the Pemgarda intervention for Patient C, Figure 6, would not be indicated for viral persistence as they clearly show a hyperimmune response. The antibody concentrations were measured at 176 mg/L prior to intervention, one of the highest levels observed and potentially close to a maximum antibody production rate for the patient. The hyperimmune response suggests some autoimmune reaction to a natural body protein or ongoing response to the production of spike protein. The Pemgarda infusion increased the concentration nearly 10fold to 1189 mg/L, correlating with almost complete relief from all symptoms within one month. The mechanisms of action consistent with the relief are persistent spike protein production which is fully complexed and removed by the presence of the Pemgarda or downregulation of autoantibody production. Either would suggest the symptom relief would depend on the concentration of Pemgarda which has a half-life of 49 days 16 . Antibody concentration will fall to the pre-intervention levels in 2.67 half-lives, or 131 days, with the potential return of symptoms.


=== INTRODUCTION ===

Long COVID is a general term for a syndrome of more than 250 symptoms that occur as post-acute sequalae to SARS-CoV-2 (PASC) exposure and potentially post-vaccination. The WHO has standardised the definition of long COVID 1 based on the persistence of symptoms 2 more than 20 weeks after confirmed infection, and if there are no other clear causes. The definition is, in part, confounded by asymptomatic infection. There are currently no objective diagnostic tests. Viral particles and mRNA have been identified in a large number of tissue and organ biopsies 3 as evidence of persistent virus. Further, circulating spike protein has been detected in the serum of 60% of long COVID patients in some studies 4 , pointing to viral reactivation mechansims 5 or to more generally mobile virus. Interventions have been tried, targeting active virus with re-purposed or new antiviral drugs and/or monoclonal antibodies to remove both spike and latent or active virus 6 . However, none of the trials have been successful, with significant difficulties in defining primary and secondary outcomes based on symptom improvement alone.
The effectiveness of antibody clearance of latent virus depends on the accurate targeting of the antibody to the persistent variant and the accessibility of the latent viral reservoirs to the antibody infusion. Tissue infusion based on plasma concentration of mAbs 7 has estimated perfusion into parts of the body as percentages of plasma concentration: lung 14.9%, heart 10.2%, kidney 13.7%, muscle 3.97%, skin 15.7%, small intestine 5.22%, large intestine 5.03%, spleen 12.8%, liver 12.1%, bone 7.27%, stomach 4.98%, lymph node 8.46%, adipose 4.78%, brain 0.351%, pancreas 6.4%, testes 5.88%, thyroid 67.5%, and thymus 6.62%. Latent viral reservoirs in these locations could account for many of the symptoms of long COVID, especially brain fog. Intramuscular and intravenous interventions provide different perfusion profiles in the patient and could influence attainment of critical antibody concentrations for viral clearance. Even with favourable perfusion, symptoms may also be slow to recover, if at all. The two most frequently proposed pathophysiologies for long COVID are viral persistence and autoimmunity [8][9][10] , both of which would likely have signatures in the antibody immunity profile of patients. Persistent virus may be explained by a cellular-based recovery from acute infection but poor antibody production to affect complete clearance of the virus from the body. The antibody response of the patient is variant specific [11][12][13] , producing antibodies following natural infection and immunisation, leading to a variant antibody spectrum in concentration as in Figure 1, which shows antibody profiles to 15 SARS-CoV-2 variants. The new antibody quality measure [11][12][13] reflects the antibody binding to the antigen, resulting in a complex that has a half-life of longer than 2 hours. It is thus a measurement of the antibody avidity in the complex.
A patient may be given a hypoimmune or immuno-compromised classification, Figure 1(a), if it shows a gap in the antibody spectrum in either quantity or quality (or both) that coincides with the onset of long COVID symptoms. The classification allows the likely persistent variant to be identified and so a targeted monoclonal antibody or vaccine therapy response. Similarly, elevated antibody levels in the spectrum following vaccination or infection and associated with the onset of long COVID symptoms, Figure 1(b), would indicate a hyperimmune or autoimmune response. Here, the intervention pathway is less clear depending on the mechanism of hyperimmunity.
Figure 1 The association of the prevalent variant wave and the antibody immunity spectrum in immunocompromised and hyperimmune patients: (a) A poor antibody spectrum of a long COVID patient can be aligned with Variant waves and compared with the onset of long COVID symptoms. The antibody spectrum shows low quantity and quality of antibodies associated with the omicron infection triggering the long COVID symptoms and leads to an assignment of the persistent variant; and (b) the onset of symptoms associated with a vaccination, with the antibody immunity concentration and avidity leading to an assignment of variant spike production based on vaccine variant target or autoimmunity following infection.
In this paper, interventional case studies are reviewed against the initial classification of the patients using a SARS-CoV-2 antibody spectrum test in either quantity or quality, the onset of symptoms, and likely exposure to the variant. Interventions to fill the endotype gaps in the antibody spectrum with monoclonal antibodies were considered as well as protein vaccine repair. In addition, a natural second infection leading to recovery is also characterised by changes in antibody spectra. Further, a hyperimmune intervention using a monoclonal antibody is also characterised. The change in symptoms is considered for each patient case. The heterogeneity of the long COVID patient cohort is considered for antibody immunity profiles, latent virus variant reservoirs, spike protein production, autoimmunity, and intervention success measures.


=== ABSTRACT ===
Interventions to 6 long COVID patients were monitored and compared with the SARS CoV 2 antibody spectrum for 15 variants for both quantity and quality (avidity):


=== TITLE ===
Long COVID Pathophysiology Interventions and Recovery Case Studies profiled with SARS-CoV-2 Spike Protein Antibody Variant Spectrum

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

Objective  
Confirm whether the manuscript includes new data or qualifies as a clinical research protocol, based on content required by medRxiv.

Instructions 
1. Analyze the manuscript for the presence of either:
	- New data from experiments, observations, clinical trials, or simulations, even if based on previously published sources (e.g., meta-analyses with new synthesis).
	- For a medRxiv submission, check whether the submission is a clinical research protocol, defined as a structured plan for a clinical study.
2. Treat meta-analyses and data syntheses from prior studies as valid forms of ""data"" for this check, if they involve original re-analysis or synthesis.
3. To confirm presence of new data:
	- Review tables, figures, or data presentations that are not directly reused from prior published work.
	- Look for explicit mentions of new findings or original analysis.
4. To confirm a clinical research protocol:
	- Check for structured information on study objectives, design, methods, eligibility criteria, or trial phases.
	- Trial registration numbers or ethics review statements are useful indicators.
5. Mark the manuscript as FAIL if neither new data nor clinical protocol indicators are found.
	- If the content is ambiguous or borderline, mark as FAIL with a clear explanation so that a human screener can review the context.

Output
1. Provide a PASS or FAIL result
	- Has New Data Or Is A Clinical Research Protocol: PASS
	- Does Not Have New Data Or A Clinical Research Protocol: FAIL
2. Provide a Detailed Explanation to evidence the PASS or FAIL result
3. Provide the context From the Paper to evidence the PASS or FAIL result

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

**SPECIFIC CHECK:**
Objective  
Confirm whether the manuscript includes new data or qualifies as a clinical research protocol, based on content required by medRxiv.

Instructions 
1. Analyze the manuscript for the presence of either:
	- New data from experiments, observations, clinical trials, or simulations, even if based on previously published sources (e.g., meta-analyses with new synthesis).
	- For a medRxiv submission, check whether the submission is a clinical research protocol, defined as a structured plan for a clinical study.
2. Treat meta-analyses and data syntheses from prior studies as valid forms of ""data"" for this check, if they involve original re-analysis or synthesis.
3. To confirm presence of new data:
	- Review tables, figures, or data presentations that are not directly reused from prior published work.
	- Look for explicit mentions of new findings or original analysis.
4. To confirm a clinical research protocol:
	- Check for structured information on study objectives, design, methods, eligibility criteria, or trial phases.
	- Trial registration numbers or ethics review statements are useful indicators.
5. Mark the manuscript as FAIL if neither new data nor clinical protocol indicators are found.
	- If the content is ambiguous or borderline, mark as FAIL with a clear explanation so that a human screener can review the context.

Output
1. Provide a PASS or FAIL result
	- Has New Data Or Is A Clinical Research Protocol: PASS
	- Does Not Have New Data Or A Clinical Research Protocol: FAIL
2. Provide a Detailed Explanation to evidence the PASS or FAIL result
3. Provide the context From the Paper to evidence the PASS or FAIL result



**DOCUMENT CONTENT TO ANALYZE:**

=== DISCUSSION ===

The alignment of the COVID multivariant antibody spectrum with the onset of symptoms provides an insight into targeting for any interventions. A hole in the antibody spectrum in either quantity or quality suggests a non-clearing serum and the risk of persistent virus. The alignment of the spectrum and onset of symptoms increases the probability of identifying which variant is responsible, notwithstanding the mutation of a unique variant during an infection. Monoclonal antibodies effective against the persistent variant are a potential therapy to plug the gap in the antibody spectrum and initiate clearance; patients A-D. Patient A is the most extensive investigation exploring both the effects of serum concentration on perfusion into body compartments, including accessing CSF, and subsequent repair of the antibody spectrum. Evusheld is well targeted at the Wuhan variant with good concentrations and high quality, Figure 2(c) and (d), leading to perfusion. The unique observation is the presence of Evusheld in the CSF, Figure 3, made possible because of the 'fingerprint' derived from the antibody spectrum. The estimated monoclonal antibody perfusion 7 into the brain of 0.351% and the observed percentage at 0.5% is striking. Perfusion into the CSF correlates with reduction of migraine frequence from daily to monthly after the second dose, and subsequent return to near-normal brain function. The patient recovery from bed-bound to approximately 80% normal function details a long journey with a number of other interventions including apheresis, further signifying the complexity and personalisation needed in the recovery journey.
The Evusheld intervention of Patient B, Figure 5, is mistargeted against the gap in the antibody spectrum with persistent Omicron BA.1/5 against which Evusheld has no efficacy; Sipavibart would have been a better drug choice as seen in the therapy of Patient D, Figure 7. However, Sipavibart is well targeted for the poor antibody quality to Wuhan in Patient D, with a dosing regimen consistent with the perfusion requirements. Sipavibart has significant antibody quality up to variants KP.3, with some gaps, Figure 7(f), although binding concentrations are low for variants after BA.1. The perfusion regimen for Sipavibart against later variants needs consideration. The dominant symptom for Patient D is dysautonomia suggesting a requirement for perfusion into the CSF. Early in the intervention, there is a significant improvement in the HRV response but 8 weeks post infusion there has been no significant improvement in symptoms. Both Sipavibart and Evusheld have had modifications to the antibody, reducing the FcγR receptor binding and mutations in the C1q binding site to improve the half-life in the blood. However, these antibody-dependent cellular cytotoxicity (ADCC) modifications 15 are required for the clearance of an antibody-virus complex.
Unlike the Patients A, B, and D who show hypo-immune or immuno-compromised antibody spectra, the Pemgarda intervention for Patient C, Figure 6, would not be indicated for viral persistence as they clearly show a hyperimmune response. The antibody concentrations were measured at 176 mg/L prior to intervention, one of the highest levels observed and potentially close to a maximum antibody production rate for the patient. The hyperimmune response suggests some autoimmune reaction to a natural body protein or ongoing response to the production of spike protein. The Pemgarda infusion increased the concentration nearly 10fold to 1189 mg/L, correlating with almost complete relief from all symptoms within one month. The mechanisms of action consistent with the relief are persistent spike protein production which is fully complexed and removed by the presence of the Pemgarda or downregulation of autoantibody production. Either would suggest the symptom relief would depend on the concentration of Pemgarda which has a half-life of 49 days 16 . Antibody concentration will fall to the pre-intervention levels in 2.67 half-lives, or 131 days, with the potential return of symptoms.


=== INTRODUCTION ===

Long COVID is a general term for a syndrome of more than 250 symptoms that occur as post-acute sequalae to SARS-CoV-2 (PASC) exposure and potentially post-vaccination. The WHO has standardised the definition of long COVID 1 based on the persistence of symptoms 2 more than 20 weeks after confirmed infection, and if there are no other clear causes. The definition is, in part, confounded by asymptomatic infection. There are currently no objective diagnostic tests. Viral particles and mRNA have been identified in a large number of tissue and organ biopsies 3 as evidence of persistent virus. Further, circulating spike protein has been detected in the serum of 60% of long COVID patients in some studies 4 , pointing to viral reactivation mechansims 5 or to more generally mobile virus. Interventions have been tried, targeting active virus with re-purposed or new antiviral drugs and/or monoclonal antibodies to remove both spike and latent or active virus 6 . However, none of the trials have been successful, with significant difficulties in defining primary and secondary outcomes based on symptom improvement alone.
The effectiveness of antibody clearance of latent virus depends on the accurate targeting of the antibody to the persistent variant and the accessibility of the latent viral reservoirs to the antibody infusion. Tissue infusion based on plasma concentration of mAbs 7 has estimated perfusion into parts of the body as percentages of plasma concentration: lung 14.9%, heart 10.2%, kidney 13.7%, muscle 3.97%, skin 15.7%, small intestine 5.22%, large intestine 5.03%, spleen 12.8%, liver 12.1%, bone 7.27%, stomach 4.98%, lymph node 8.46%, adipose 4.78%, brain 0.351%, pancreas 6.4%, testes 5.88%, thyroid 67.5%, and thymus 6.62%. Latent viral reservoirs in these locations could account for many of the symptoms of long COVID, especially brain fog. Intramuscular and intravenous interventions provide different perfusion profiles in the patient and could influence attainment of critical antibody concentrations for viral clearance. Even with favourable perfusion, symptoms may also be slow to recover, if at all. The two most frequently proposed pathophysiologies for long COVID are viral persistence and autoimmunity [8][9][10] , both of which would likely have signatures in the antibody immunity profile of patients. Persistent virus may be explained by a cellular-based recovery from acute infection but poor antibody production to affect complete clearance of the virus from the body. The antibody response of the patient is variant specific [11][12][13] , producing antibodies following natural infection and immunisation, leading to a variant antibody spectrum in concentration as in Figure 1, which shows antibody profiles to 15 SARS-CoV-2 variants. The new antibody quality measure [11][12][13] reflects the antibody binding to the antigen, resulting in a complex that has a half-life of longer than 2 hours. It is thus a measurement of the antibody avidity in the complex.
A patient may be given a hypoimmune or immuno-compromised classification, Figure 1(a), if it shows a gap in the antibody spectrum in either quantity or quality (or both) that coincides with the onset of long COVID symptoms. The classification allows the likely persistent variant to be identified and so a targeted monoclonal antibody or vaccine therapy response. Similarly, elevated antibody levels in the spectrum following vaccination or infection and associated with the onset of long COVID symptoms, Figure 1(b), would indicate a hyperimmune or autoimmune response. Here, the intervention pathway is less clear depending on the mechanism of hyperimmunity.
Figure 1 The association of the prevalent variant wave and the antibody immunity spectrum in immunocompromised and hyperimmune patients: (a) A poor antibody spectrum of a long COVID patient can be aligned with Variant waves and compared with the onset of long COVID symptoms. The antibody spectrum shows low quantity and quality of antibodies associated with the omicron infection triggering the long COVID symptoms and leads to an assignment of the persistent variant; and (b) the onset of symptoms associated with a vaccination, with the antibody immunity concentration and avidity leading to an assignment of variant spike production based on vaccine variant target or autoimmunity following infection.
In this paper, interventional case studies are reviewed against the initial classification of the patients using a SARS-CoV-2 antibody spectrum test in either quantity or quality, the onset of symptoms, and likely exposure to the variant. Interventions to fill the endotype gaps in the antibody spectrum with monoclonal antibodies were considered as well as protein vaccine repair. In addition, a natural second infection leading to recovery is also characterised by changes in antibody spectra. Further, a hyperimmune intervention using a monoclonal antibody is also characterised. The change in symptoms is considered for each patient case. The heterogeneity of the long COVID patient cohort is considered for antibody immunity profiles, latent virus variant reservoirs, spike protein production, autoimmunity, and intervention success measures.


=== ABSTRACT ===
Interventions to 6 long COVID patients were monitored and compared with the SARS CoV 2 antibody spectrum for 15 variants for both quantity and quality (avidity):


=== TITLE ===
Long COVID Pathophysiology Interventions and Recovery Case Studies profiled with SARS-CoV-2 Spike Protein Antibody Variant Spectrum

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents original clinical observations and quantitative measurements from six long‑COVID patients, including antibody spectra, perfusion estimates, and therapeutic outcomes. These data are not merely re‑analysis of previously published results; they are newly collected patient‑specific findings and are presented in figures and narrative. The paper therefore contains new data and does not need to be a formal clinical research protocol.  
**CONTEXT:**  
- “Interventions to 6 long COVID patients were monitored and compared with the SARS CoV 2 antibody spectrum for 15 variants for both quantity and quality (avidity).”  
- Discussion excerpts: “The unique observation is the presence of Evusheld in the CSF, Figure 3…”, “The Evusheld intervention of Patient B, Figure 5…”, “The Pemgarda infusion increased the concentration nearly 10fold to 1189 mg/L, correlating with almost complete relief from all symptoms within one month.”  
- Figures referenced (2,3,5,6,7) and associated quantitative data (e.g., perfusion 0.351% into brain, antibody concentrations 176 mg/L → 1189 mg/L).

**RESULT:** PASS  
**EXPLANATION:** The manuscript presents original clinical observations from six long‑COVID patients, including new antibody‑spectrum measurements, perfusion estimates, and therapeutic outcomes. These data are not reproduced from prior publications and are presented in figures and narrative text, qualifying the submission as containing new data.  
**CONTEXT:**  
- “Interventions to 6 long COVID patients were monitored and compared with the SARS CoV‑2 antibody spectrum for 15 variants for both quantity and quality (avidity)” (Abstract).  
- “Patient A is the most extensive investigation exploring both the effects of serum concentration on perfusion into body compartments, including accessing CSF, and subsequent repair of the antibody spectrum.” (Discussion).  
- “The unique observation is the presence of Evusheld in the CSF, Figure 3, made possible because of the 'fingerprint' derived from the antibody spectrum.” (Discussion).  
- “The Evusheld intervention of Patient B, Figure 5, is mistargeted against the gap in the antibody spectrum…” (Discussion).  
- “The Pemgarda intervention for Patient C, Figure 6, would not be indicated for viral persistence…” (Discussion).  
These excerpts demonstrate that the manuscript reports original, patient‑specific data rather than a review or protocol alone.

=== ABSTRACT ===
According to Scopus, from January 1990 to February 2025, over 182,000 scientific articles referenced 'policy makers' or 'decision makers' in their title, abstract, or keywords. Some may assume that these terms refer to individuals with the ability to make decisions about government or institutional policy, informed in part by scientific information, but more than that is usually left unsaid. This audience is for example often not even defined in scientific articles. The vague abstraction of who in government can use scientific information, and in what ways, hides the true complexity and heterogeneity of real governmental systems and the roles within them. Such simplifications limit scientists' ability to support evidence-based policy. Here we argue that it is time for scientists to move beyond viewing 'government' as a single, uniform, and abstract entity and instead recognise the diverse interests and roles within it. By doing so scientists will be able to contribute more effectively to government policy, both during a crisis and in the calmer times between 1 . During the COVID-19 pandemic, the interface between science and government was highly visible and intensely stressed. Scientists worldwide were abruptly thrust to the fore of a government decision-making process that was hectic, complex, and for some unfamiliar. The authors of this article are scientists who were involved in the pandemic response in 14 different countries representing every region of the globe. Through these experiences and other crisis and non-crisis work since, we gained important insights into how scientists might better understand the complex structure of government decision-making and the diversity of actors involved 2 . Building on these experiences and a qualitative study of modelling-to-policy interactions [1], we present a general framework to help scientists better understand this diversity of actors


=== TITLE ===
The elusive 'policy maker' How understanding the diversity of perspectives and systems in governments can increase the impact of scientific research

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

Objective  
Confirm whether the manuscript qualifies as a research paper or a systematic review based on structural and methodological characteristics.

Instructions  
1. Evaluate the manuscript for the following:
 - A research paper presents original research findings based on experiments, observations, or analyses conducted by the authors.
 - A systematic review provides a structured review of prior research following a clear methodology (e.g., inclusion/exclusion criteria, data synthesis).
2. For systematic reviews, look for indicators such as:
 - Mention of search strategies, search terms, or databases used.
 - Use of established frameworks like PRISMA, PICO, or equivalent (optional but useful indicators).
3. Check for presence of standard sections:
 - Abstract, Introduction, Methods, Results, Discussion/Conclusion.
 - Accept variants such as Background, Materials and Methods, Findings, or Conclusions.
4. Look for language suggesting original research or systematic methodology, such as:
 - “we conducted experiments,” “our findings show,” “we systematically reviewed,” “studies were selected based on.”
5. Mark the manuscript as FAIL if it lacks structural and methodological indicators of original research or systematic review.
- Narrative summaries, essays, or theory pieces without structured methodology should be marked FAIL.
6.  If it IS a research paper or a systematic review, mark as PASS. If it is NOT a research paper or a systematic review, mark as FAIL.

Output Format
- PASS / FAIL
- Detailed Explanation
- Context From the Paper

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

**SPECIFIC CHECK:**
Objective  
Confirm whether the manuscript qualifies as a research paper or a systematic review based on structural and methodological characteristics.

Instructions  
1. Evaluate the manuscript for the following:
 - A research paper presents original research findings based on experiments, observations, or analyses conducted by the authors.
 - A systematic review provides a structured review of prior research following a clear methodology (e.g., inclusion/exclusion criteria, data synthesis).
2. For systematic reviews, look for indicators such as:
 - Mention of search strategies, search terms, or databases used.
 - Use of established frameworks like PRISMA, PICO, or equivalent (optional but useful indicators).
3. Check for presence of standard sections:
 - Abstract, Introduction, Methods, Results, Discussion/Conclusion.
 - Accept variants such as Background, Materials and Methods, Findings, or Conclusions.
4. Look for language suggesting original research or systematic methodology, such as:
 - “we conducted experiments,” “our findings show,” “we systematically reviewed,” “studies were selected based on.”
5. Mark the manuscript as FAIL if it lacks structural and methodological indicators of original research or systematic review.
- Narrative summaries, essays, or theory pieces without structured methodology should be marked FAIL.
6.  If it IS a research paper or a systematic review, mark as PASS. If it is NOT a research paper or a systematic review, mark as FAIL.

Output Format
- PASS / FAIL
- Detailed Explanation
- Context From the Paper

**DOCUMENT CONTENT TO ANALYZE:**

=== ABSTRACT ===
According to Scopus, from January 1990 to February 2025, over 182,000 scientific articles referenced 'policy makers' or 'decision makers' in their title, abstract, or keywords. Some may assume that these terms refer to individuals with the ability to make decisions about government or institutional policy, informed in part by scientific information, but more than that is usually left unsaid. This audience is for example often not even defined in scientific articles. The vague abstraction of who in government can use scientific information, and in what ways, hides the true complexity and heterogeneity of real governmental systems and the roles within them. Such simplifications limit scientists' ability to support evidence-based policy. Here we argue that it is time for scientists to move beyond viewing 'government' as a single, uniform, and abstract entity and instead recognise the diverse interests and roles within it. By doing so scientists will be able to contribute more effectively to government policy, both during a crisis and in the calmer times between 1 . During the COVID-19 pandemic, the interface between science and government was highly visible and intensely stressed. Scientists worldwide were abruptly thrust to the fore of a government decision-making process that was hectic, complex, and for some unfamiliar. The authors of this article are scientists who were involved in the pandemic response in 14 different countries representing every region of the globe. Through these experiences and other crisis and non-crisis work since, we gained important insights into how scientists might better understand the complex structure of government decision-making and the diversity of actors involved 2 . Building on these experiences and a qualitative study of modelling-to-policy interactions [1], we present a general framework to help scientists better understand this diversity of actors


=== TITLE ===
The elusive 'policy maker' How understanding the diversity of perspectives and systems in governments can increase the impact of scientific research

**RESULT:** FAIL  
**EXPLANATION:** The manuscript presents a conceptual discussion and framework based on the authors’ experiences and a qualitative study, but it lacks the structured methodology, data collection, analysis, or systematic review procedures required for a research paper or systematic review.  
**CONTEXT:** “The authors of this article are scientists who were involved in the pandemic response in 14 different countries…Through these experiences and other crisis and non-crisis work since, we gained important insights…Building on these experiences and a qualitative study of modelling-to-policy interactions [1], we present a general framework to help scientists better understand this diversity of actors.”

**RESULT:** FAIL  
**EXPLANATION:** The manuscript lacks the structural and methodological features of an original research paper or a systematic review. It does not describe a specific study design, data collection, analysis, or systematic search strategy, nor does it present results from experiments or observations. Instead, it offers a conceptual framework based on authors’ experiences and a cited qualitative study, characteristic of a commentary or perspective piece.  
**CONTEXT:** “The authors of this article are scientists who were involved in the pandemic response in 14 different countries … Through these experiences and other crisis and non‑crisis work since, we gained important insights … Building on these experiences and a qualitative study of modelling‑to‑policy interactions [1], we present a general framework …”

=== ABSTRACT ===
According to Scopus, from January 1990 to February 2025, over 182,000 scientific articles referenced 'policy makers' or 'decision makers' in their title, abstract, or keywords. Some may assume that these terms refer to individuals with the ability to make decisions about government or institutional policy, informed in part by scientific information, but more than that is usually left unsaid. This audience is for example often not even defined in scientific articles. The vague abstraction of who in government can use scientific information, and in what ways, hides the true complexity and heterogeneity of real governmental systems and the roles within them. Such simplifications limit scientists' ability to support evidence-based policy. Here we argue that it is time for scientists to move beyond viewing 'government' as a single, uniform, and abstract entity and instead recognise the diverse interests and roles within it. By doing so scientists will be able to contribute more effectively to government policy, both during a crisis and in the calmer times between 1 . During the COVID-19 pandemic, the interface between science and government was highly visible and intensely stressed. Scientists worldwide were abruptly thrust to the fore of a government decision-making process that was hectic, complex, and for some unfamiliar. The authors of this article are scientists who were involved in the pandemic response in 14 different countries representing every region of the globe. Through these experiences and other crisis and non-crisis work since, we gained important insights into how scientists might better understand the complex structure of government decision-making and the diversity of actors involved 2 . Building on these experiences and a qualitative study of modelling-to-policy interactions [1], we present a general framework to help scientists better understand this diversity of actors


=== TITLE ===
The elusive 'policy maker' How understanding the diversity of perspectives and systems in governments can increase the impact of scientific research

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

Objective  
Confirm whether the manuscript includes new data or qualifies as a clinical research protocol, based on content required by medRxiv.

Instructions 
1. Analyze the manuscript for the presence of either:
	- New data from experiments, observations, clinical trials, or simulations, even if based on previously published sources (e.g., meta-analyses with new synthesis).
	- For a medRxiv submission, check whether the submission is a clinical research protocol, defined as a structured plan for a clinical study.
2. Treat meta-analyses and data syntheses from prior studies as valid forms of ""data"" for this check, if they involve original re-analysis or synthesis.
3. To confirm presence of new data:
	- Review tables, figures, or data presentations that are not directly reused from prior published work.
	- Look for explicit mentions of new findings or original analysis.
4. To confirm a clinical research protocol:
	- Check for structured information on study objectives, design, methods, eligibility criteria, or trial phases.
	- Trial registration numbers or ethics review statements are useful indicators.
5. Mark the manuscript as FAIL if neither new data nor clinical protocol indicators are found.
	- If the content is ambiguous or borderline, mark as FAIL with a clear explanation so that a human screener can review the context.

Output
1. Provide a PASS or FAIL result
	- Has New Data Or Is A Clinical Research Protocol: PASS
	- Does Not Have New Data Or A Clinical Research Protocol: FAIL
2. Provide a Detailed Explanation to evidence the PASS or FAIL result
3. Provide the context From the Paper to evidence the PASS or FAIL result

**General Instructions:**
You are an AI pre-print screening assistant tasked with evaluating a submission for its suitability for acceptance on a pre-print server. Your primary goal is to answer the individual screening checks using evidence directly from the documents, which may include the manuscript, supplementary information, and metadata. You must avoid generating information that is not explicitly stated in the document. Ensure your response is context-driven, concise, and accurate.

Your check result must follow this EXACT format:
**RESULT:** [PASS/FAIL]
**EXPLANATION:** [Brief explanation of why it passed or failed]
**CONTEXT:** [The specific text/evidence from the document that supports your result]

Do not deviate from this format. Always provide exactly one PASS or FAIL result.

**SPECIFIC CHECK:**
Objective  
Confirm whether the manuscript includes new data or qualifies as a clinical research protocol, based on content required by medRxiv.

Instructions 
1. Analyze the manuscript for the presence of either:
	- New data from experiments, observations, clinical trials, or simulations, even if based on previously published sources (e.g., meta-analyses with new synthesis).
	- For a medRxiv submission, check whether the submission is a clinical research protocol, defined as a structured plan for a clinical study.
2. Treat meta-analyses and data syntheses from prior studies as valid forms of ""data"" for this check, if they involve original re-analysis or synthesis.
3. To confirm presence of new data:
	- Review tables, figures, or data presentations that are not directly reused from prior published work.
	- Look for explicit mentions of new findings or original analysis.
4. To confirm a clinical research protocol:
	- Check for structured information on study objectives, design, methods, eligibility criteria, or trial phases.
	- Trial registration numbers or ethics review statements are useful indicators.
5. Mark the manuscript as FAIL if neither new data nor clinical protocol indicators are found.
	- If the content is ambiguous or borderline, mark as FAIL with a clear explanation so that a human screener can review the context.

Output
1. Provide a PASS or FAIL result
	- Has New Data Or Is A Clinical Research Protocol: PASS
	- Does Not Have New Data Or A Clinical Research Protocol: FAIL
2. Provide a Detailed Explanation to evidence the PASS or FAIL result
3. Provide the context From the Paper to evidence the PASS or FAIL result



**DOCUMENT CONTENT TO ANALYZE:**

=== ABSTRACT ===
According to Scopus, from January 1990 to February 2025, over 182,000 scientific articles referenced 'policy makers' or 'decision makers' in their title, abstract, or keywords. Some may assume that these terms refer to individuals with the ability to make decisions about government or institutional policy, informed in part by scientific information, but more than that is usually left unsaid. This audience is for example often not even defined in scientific articles. The vague abstraction of who in government can use scientific information, and in what ways, hides the true complexity and heterogeneity of real governmental systems and the roles within them. Such simplifications limit scientists' ability to support evidence-based policy. Here we argue that it is time for scientists to move beyond viewing 'government' as a single, uniform, and abstract entity and instead recognise the diverse interests and roles within it. By doing so scientists will be able to contribute more effectively to government policy, both during a crisis and in the calmer times between 1 . During the COVID-19 pandemic, the interface between science and government was highly visible and intensely stressed. Scientists worldwide were abruptly thrust to the fore of a government decision-making process that was hectic, complex, and for some unfamiliar. The authors of this article are scientists who were involved in the pandemic response in 14 different countries representing every region of the globe. Through these experiences and other crisis and non-crisis work since, we gained important insights into how scientists might better understand the complex structure of government decision-making and the diversity of actors involved 2 . Building on these experiences and a qualitative study of modelling-to-policy interactions [1], we present a general framework to help scientists better understand this diversity of actors


=== TITLE ===
The elusive 'policy maker' How understanding the diversity of perspectives and systems in governments can increase the impact of scientific research

**RESULT:** FAIL  
**EXPLANATION:** The manuscript does not present any new experimental, observational, clinical, or simulation data, nor does it describe a structured clinical research protocol. The abstract and title describe a conceptual framework derived from authors’ experiences and a prior qualitative study, without any new data tables, figures, or protocol details.  
**CONTEXT:** “Through these experiences and other crisis and non‑crisis work since, we gained important insights into how scientists might better understand the complex structure of government decision‑making and the diversity of actors involved… Building on these experiences and a qualitative study of modelling‑to‑policy interactions [1], we present a general framework to help scientists better understand this diversity of actors.”

**RESULT:** FAIL  
**EXPLANATION:** The manuscript does not present any new experimental, observational, clinical, or simulation data, nor does it outline a structured plan for a clinical study. It is a conceptual framework paper drawing on authors’ experiences and a qualitative study, without tables, figures, or original data analysis.  
**CONTEXT:** “Through these experiences and other crisis and non‑crisis work since, we gained important insights into how scientists might better understand the complex structure of government decision‑making and the diversity of actors involved … Building on these experiences and a qualitative study of modelling‑to‑policy interactions [1], we present a general framework to help scientists better understand this diversity of actors.”