JMIR Medical Education最新文献

{"title":"Evaluation of ChatGPT-Generated Differential Diagnosis for Common Diseases With Atypical Presentation: Descriptive Research.","authors":"Kiyoshi Shikino, Taro Shimizu, Yuki Otsuka, Masaki Tago, Hiromizu Takahashi, Takashi Watari, Yosuke Sasaki, Gemmei Iizuka, Hiroki Tamura, Koichi Nakashima, Kotaro Kunitomo, Morika Suzuki, Sayaka Aoyama, Shintaro Kosaka, Teiko Kawahigashi, Tomohiro Matsumoto, Fumina Orihara, Toru Morikawa, Toshinori Nishizawa, Yoji Hoshina, Yu Yamamoto, Yuichiro Matsuo, Yuto Unoki, Hirofumi Kimura, Midori Tokushima, Satoshi Watanuki, Takuma Saito, Fumio Otsuka, Yasuharu Tokuda","doi":"10.2196/58758","DOIUrl":"10.2196/58758","url":null,"abstract":"<p><strong>Background: </strong>The persistence of diagnostic errors, despite advances in medical knowledge and diagnostics, highlights the importance of understanding atypical disease presentations and their contribution to mortality and morbidity. Artificial intelligence (AI), particularly generative pre-trained transformers like GPT-4, holds promise for improving diagnostic accuracy, but requires further exploration in handling atypical presentations.</p><p><strong>Objective: </strong>This study aimed to assess the diagnostic accuracy of ChatGPT in generating differential diagnoses for atypical presentations of common diseases, with a focus on the model's reliance on patient history during the diagnostic process.</p><p><strong>Methods: </strong>We used 25 clinical vignettes from the Journal of Generalist Medicine characterizing atypical manifestations of common diseases. Two general medicine physicians categorized the cases based on atypicality. ChatGPT was then used to generate differential diagnoses based on the clinical information provided. The concordance between AI-generated and final diagnoses was measured, with a focus on the top-ranked disease (top 1) and the top 5 differential diagnoses (top 5).</p><p><strong>Results: </strong>ChatGPT's diagnostic accuracy decreased with an increase in atypical presentation. For category 1 (C1) cases, the concordance rates were 17% (n=1) for the top 1 and 67% (n=4) for the top 5. Categories 3 (C3) and 4 (C4) showed a 0% concordance for top 1 and markedly lower rates for the top 5, indicating difficulties in handling highly atypical cases. The χ2 test revealed no significant difference in the top 1 differential diagnosis accuracy between less atypical (C1+C2) and more atypical (C3+C4) groups (χ²1=2.07; n=25; P=.13). However, a significant difference was found in the top 5 analyses, with less atypical cases showing higher accuracy (χ²1=4.01; n=25; P=.048).</p><p><strong>Conclusions: </strong>ChatGPT-4 demonstrates potential as an auxiliary tool for diagnosing typical and mildly atypical presentations of common diseases. However, its performance declines with greater atypicality. The study findings underscore the need for AI systems to encompass a broader range of linguistic capabilities, cultural understanding, and diverse clinical scenarios to improve diagnostic utility in real-world settings.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11199925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141447236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Novel Web-Based Tool to Enhance Clinical Skills in Medical Education.","authors":"Ayma Aqib, Faiha Fareez, Elnaz Assadpour, Tubba Babar, Andrew Kokavec, Edward Wang, Thomas Lo, Jean-Paul Lam, Christopher Smith","doi":"10.2196/47438","DOIUrl":"10.2196/47438","url":null,"abstract":"<p><strong>Unlabelled: </strong>A significant component of Canadian medical education is the development of clinical skills. The medical educational curriculum assesses these skills through an objective structured clinical examination (OSCE). This OSCE assesses skills imperative to good clinical practice, such as patient communication, clinical decision-making, and medical knowledge. Despite the widespread implementation of this examination across all academic settings, few preparatory resources exist that cater specifically to Canadian medical students. MonkeyJacket is a novel, open-access, web-based application, built with the goal of providing medical students with an accessible and representative tool for clinical skill development for the OSCE and clinical settings. This viewpoint paper presents the development of the MonkeyJacket application and its potential to assist medical students in preparation for clinical examinations and practical settings. Limited resources exist that are web-based; accessible in terms of cost; specific to the Medical Council of Canada (MCC); and, most importantly, scalable in nature. The goal of this research study was to thoroughly describe the potential utility of the application, particularly its capacity to provide practice and scalable formative feedback to medical students. MonkeyJacket was developed to provide Canadian medical students with the opportunity to practice their clinical examination skills and receive peer feedback by using a centralized platform. The OSCE cases included in the application were developed by using the MCC guidelines to ensure their applicability to a Canadian setting. There are currently 75 cases covering 5 specialties, including cardiology, respirology, gastroenterology, neurology, and psychiatry. The MonkeyJacket application is a web-based platform that allows medical students to practice clinical decision-making skills in real time with their peers through a synchronous platform. Through this application, students can practice patient interviewing, clinical reasoning, developing differential diagnoses, and formulating a management plan, and they can receive both qualitative feedback and quantitative feedback. Each clinical case is associated with an assessment checklist that is accessible to students after practice sessions are complete; the checklist promotes personal improvement through peer feedback. This tool provides students with relevant case stems, follow-up questions that probe for differential diagnoses and management plans, assessment checklists, and the ability to review the trend in their performance. The MonkeyJacket application provides medical students with a valuable tool that promotes clinical skill development for OSCEs and clinical settings. MonkeyJacket introduces a way for medical learners to receive feedback regarding patient interviewing and clinical reasoning skills that is both formative and scalable in nature, in addition to promoting interinstituti","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11217162/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141433025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Health Care Workers' Motivations for Enrolling in Massive Open Online Courses During a Public Health Emergency: Descriptive Analysis.","authors":"Jennifer Jones, Jamie Sewan Johnston, Ngouille Yabsa Ndiaye, Anna Tokar, Saumya Singla, Nadine Ann Skinner, Matthew Strehlow, Heini Utunen","doi":"10.2196/51915","DOIUrl":"10.2196/51915","url":null,"abstract":"<p><strong>Background: </strong>Massive open online courses (MOOCs) are increasingly used to educate health care workers during public health emergencies. In early 2020, the World Health Organization (WHO) developed a series of MOOCs for COVID-19, introducing the disease and strategies to control its outbreak, with 6 courses specifically targeting health care workers as learners. In 2020, Stanford University also launched a MOOC designed to deliver accurate and timely education on COVID-19, equipping health care workers across the globe to provide health care safely and effectively to patients with the novel infectious disease. Although the use of MOOCs for just-in-time training has expanded during the pandemic, evidence is limited regarding the factors motivating health care workers to enroll in and complete courses, particularly in low-income countries (LICs) and lower-middle-income countries (LMICs).</p><p><strong>Objective: </strong>This study seeks to gain insights on the characteristics and motivations of learners turning to MOOCs for just-in-time training, to provide evidence that can better inform MOOC design to meet the needs of health care workers. We examine data from learners in 1 Stanford University and 6 WHO COVID-19 courses to identify (1) the characteristics of health care workers completing the courses and (2) the factors motivating them to enroll.</p><p><strong>Methods: </strong>We analyze (1) course registration data of the 49,098 health care workers who completed the 7 focal courses and (2) survey responses from 6272 course completers. The survey asked respondents to rank their motivations for enrollment and share feedback about their learning experience. We use descriptive statistics to compare responses by health care profession and by World Bank country income classification.</p><p><strong>Results: </strong>Health care workers completed the focal courses from all regions of the world, with nearly one-third (14,159/49,098, 28.84%) practicing in LICs and LMICs. Survey data revealed a diverse range of professional roles among the learners, including physicians (2171/6272, 34.61%); nurses (1599/6272, 25.49%); and other health care professionals such as allied health professionals, community health workers, paramedics, and pharmacists (2502/6272, 39.89%). Across all health care professions, the primary motivation to enroll was for personal learning to improve clinical practice. Continuing education credit was also an important motivator, particularly for nonphysicians and learners in LICs and LMICs. Course cost (3423/6272, 54.58%) and certification (4238/6272, 67.57%) were also important to a majority of learners.</p><p><strong>Conclusions: </strong>Our results demonstrate that a diverse range of health care professionals accessed MOOCs for just-in-time training during a public health emergency. Although all health care workers were motivated to improve their clinical practice, different factors were influential across professio","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11199926/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141433026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Call for a Health Data-Informed Workforce Among Clinicians.","authors":"Joy Doll, A Jerrod Anzalone, Martina Clarke, Kathryn Cooper, Ann Polich, Jacob Siedlik","doi":"10.2196/52290","DOIUrl":"10.2196/52290","url":null,"abstract":"<p><strong>Unlabelled: </strong>A momentous amount of health data has been and is being collected. Across all levels of health care, data are driving decision-making and impacting patient care. A new field of knowledge and role for those in health care is emerging-the need for a health data-informed workforce. In this viewpoint, we describe the approaches needed to build a health data-informed workforce, a new and critical skill for the health care ecosystem.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11194696/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolution of Chatbots in Nursing Education: Narrative Review.","authors":"Fang Zhang, Xiaoliu Liu, Wenyan Wu, Shiben Zhu","doi":"10.2196/54987","DOIUrl":"10.2196/54987","url":null,"abstract":"<p><strong>Background: </strong>The integration of chatbots in nursing education is a rapidly evolving area with potential transformative impacts. This narrative review aims to synthesize and analyze the existing literature on chatbots in nursing education.</p><p><strong>Objective: </strong>This study aims to comprehensively examine the temporal trends, international distribution, study designs, and implications of chatbots in nursing education.</p><p><strong>Methods: </strong>A comprehensive search was conducted across 3 databases (PubMed, Web of Science, and Embase) following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram.</p><p><strong>Results: </strong>A total of 40 articles met the eligibility criteria, with a notable increase of publications in 2023 (n=28, 70%). Temporal analysis revealed a notable surge in publications from 2021 to 2023, emphasizing the growing scholarly interest. Geographically, Taiwan province made substantial contributions (n=8, 20%), followed by the United States (n=6, 15%) and South Korea (n=4, 10%). Study designs varied, with reviews (n=8, 20%) and editorials (n=7, 18%) being predominant, showcasing the richness of research in this domain.</p><p><strong>Conclusions: </strong>Integrating chatbots into nursing education presents a promising yet relatively unexplored avenue. This review highlights the urgent need for original research, emphasizing the importance of ethical considerations.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11186796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing AI Awareness and Identifying Essential Competencies: Insights From Key Stakeholders in Integrating AI Into Medical Education.","authors":"Julia-Astrid Moldt, Teresa Festl-Wietek, Wolfgang Fuhl, Susanne Zabel, Manfred Claassen, Samuel Wagner, Kay Nieselt, Anne Herrmann-Werner","doi":"10.2196/58355","DOIUrl":"10.2196/58355","url":null,"abstract":"<p><strong>Background: </strong>The increasing importance of artificial intelligence (AI) in health care has generated a growing need for health care professionals to possess a comprehensive understanding of AI technologies, requiring an adaptation in medical education.</p><p><strong>Objective: </strong>This paper explores stakeholder perceptions and expectations regarding AI in medicine and examines their potential impact on the medical curriculum. This study project aims to assess the AI experiences and awareness of different stakeholders and identify essential AI-related topics in medical education to define necessary competencies for students.</p><p><strong>Methods: </strong>The empirical data were collected as part of the TüKITZMed project between August 2022 and March 2023, using a semistructured qualitative interview. These interviews were administered to a diverse group of stakeholders to explore their experiences and perspectives of AI in medicine. A qualitative content analysis of the collected data was conducted using MAXQDA software.</p><p><strong>Results: </strong>Semistructured interviews were conducted with 38 participants (6 lecturers, 9 clinicians, 10 students, 6 AI experts, and 7 institutional stakeholders). The qualitative content analysis revealed 6 primary categories with a total of 24 subcategories to answer the research questions. The evaluation of the stakeholders' statements revealed several commonalities and differences regarding their understanding of AI. Crucial identified AI themes based on the main categories were as follows: possible curriculum contents, skills, and competencies; programming skills; curriculum scope; and curriculum structure.</p><p><strong>Conclusions: </strong>The analysis emphasizes integrating AI into medical curricula to ensure students' proficiency in clinical applications. Standardized AI comprehension is crucial for defining and teaching relevant content. Considering diverse perspectives in implementation is essential to comprehensively define AI in the medical context, addressing gaps and facilitating effective solutions for future AI use in medical studies. The results provide insights into potential curriculum content and structure, including aspects of AI in medicine.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11238140/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Anesthesia Provider Preferences for Precision Feedback: Preference Elicitation Study.","authors":"Zach Landis-Lewis, Chris A Andrews, Colin A Gross, Charles P Friedman, Nirav J Shah","doi":"10.2196/54071","DOIUrl":"10.2196/54071","url":null,"abstract":"<p><strong>Background: </strong>Health care professionals must learn continuously as a core part of their work. As the rate of knowledge production in biomedicine increases, better support for health care professionals' continuous learning is needed. In health systems, feedback is pervasive and is widely considered to be essential for learning that drives improvement. Clinical quality dashboards are one widely deployed approach to delivering feedback, but engagement with these systems is commonly low, reflecting a limited understanding of how to improve the effectiveness of feedback about health care. When coaches and facilitators deliver feedback for improving performance, they aim to be responsive to the recipient's motivations, information needs, and preferences. However, such functionality is largely missing from dashboards and feedback reports. Precision feedback is the delivery of high-value, motivating performance information that is prioritized based on its motivational potential for a specific recipient, including their needs and preferences. Anesthesia care offers a clinical domain with high-quality performance data and an abundance of evidence-based quality metrics.</p><p><strong>Objective: </strong>The objective of this study is to explore anesthesia provider preferences for precision feedback.</p><p><strong>Methods: </strong>We developed a test set of precision feedback messages with balanced characteristics across 4 performance scenarios. We created an experimental design to expose participants to contrasting message versions. We recruited anesthesia providers and elicited their preferences through analysis of the content of preferred messages. Participants additionally rated their perceived benefit of preferred messages to clinical practice on a 5-point Likert scale.</p><p><strong>Results: </strong>We elicited preferences and feedback message benefit ratings from 35 participants. Preferences were diverse across participants but largely consistent within participants. Participants' preferences were consistent for message temporality (α=.85) and display format (α=.80). Ratings of participants' perceived benefit to clinical practice of preferred messages were high (mean rating 4.27, SD 0.77).</p><p><strong>Conclusions: </strong>Health care professionals exhibited diverse yet internally consistent preferences for precision feedback across a set of performance scenarios, while also giving messages high ratings of perceived benefit. A \"one-size-fits-most approach\" to performance feedback delivery would not appear to satisfy these preferences. Precision feedback systems may hold potential to improve support for health care professionals' continuous learning by accommodating feedback preferences.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11185285/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Authors' Reply: A Use Case for Generative AI in Medical Education.","authors":"Tricia Pendergrast, Zachary Chalmers","doi":"10.2196/58370","DOIUrl":"10.2196/58370","url":null,"abstract":"","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11185278/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141301774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Use Case for Generative AI in Medical Education.","authors":"Tejas C Sekhar, Yash R Nayak, Emily A Abdoler","doi":"10.2196/56117","DOIUrl":"10.2196/56117","url":null,"abstract":"","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11185281/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141301773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global Trends in mHealth and Medical Education Research: Bibliometrics and Knowledge Graph Analysis.","authors":"Yuanhang He, Zhihong Xie, Jiachen Li, Ziang Meng, Dongbo Xue, Chenjun Hao","doi":"10.2196/52461","DOIUrl":"10.2196/52461","url":null,"abstract":"<p><strong>Background: </strong>Mobile health (mHealth) is an emerging mobile communication and networking technology for health care systems. The integration of mHealth in medical education is growing extremely rapidly, bringing new changes to the field. However, no study has analyzed the publication and research trends occurring in both mHealth and medical education.</p><p><strong>Objective: </strong>The aim of this study was to summarize the current application and development trends of mHealth in medical education by searching and analyzing published articles related to both mHealth and medical education.</p><p><strong>Methods: </strong>The literature related to mHealth and medical education published from 2003 to 2023 was searched in the Web of Science core database, and 790 articles were screened according to the search strategy. The HistCite Pro 2.0 tool was used to analyze bibliometric indicators. VOSviewer, Pajek64, and SCImago Graphica software were used to visualize research trends and identify hot spots in the field.</p><p><strong>Results: </strong>In the past two decades, the number of published papers on mHealth in medical education has gradually increased, from only 3 papers in 2003 to 130 in 2022; this increase became particularly evident in 2007. The global citation score was determined to be 10,600, with an average of 13.42 citations per article. The local citation score was 96. The United States is the country with the most widespread application of mHealth in medical education, and most of the institutions conducting in-depth research in this field are also located in the United States, closely followed by China and the United Kingdom. Based on current trends, global coauthorship and research exchange will likely continue to expand. Among the research journals publishing in this joint field, journals published by JMIR Publications have an absolute advantage. A total of 105 keywords were identified, which were divided into five categories pointing to different research directions.</p><p><strong>Conclusions: </strong>Under the influence of COVID-19, along with the popularization of smartphones and modern communication technology, the field of combining mHealth and medical education has become a more popular research direction. The concept and application of digital health will be promoted in future developments of medical education.</p>","PeriodicalId":36236,"journal":{"name":"JMIR Medical Education","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11185284/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141262713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}