Medical Education Online最新文献

{"title":"How cultural factors affect medical students' interactions with clinical practice feedback: a qualitative study of ethnically diverse students at three transcontinental campuses.","authors":"Muirne Spooner, Ciarán Reinhardt, Judith Strawbridge, James Larkin, Siaw Cheok Liew, Mohamed Hasif Jafar, Teresa Pawlikowska","doi":"10.1080/10872981.2025.2567075","DOIUrl":"10.1080/10872981.2025.2567075","url":null,"abstract":"<p><p>Transnational medical educational programs are now commonplace. Given the importance of individualised feedback, this study explored how cultural factors influence feedback experiences across three transnational campuses of one medical school. A total of 57 final-year medical students were interviewed from a sampling frame of 514 (269 male, 245 female). One-to-one semi-structured interviews were conducted and analysed using template analysis. Codes were iteratively refined into four themes, which were critically reviewed through the lenses of Hofstede's cultural dimensions and Figured Worlds. Four themes were identified: (1) early socialisation into feedback shaped by family and schooling; (2) hierarchical learning environments positioning students as passive recipients; (3) strategies to cope with negative feedback; and (4) gendered differences in the interpretation of feedback. National identity played only a minor role, while prior experiences and hierarchical structures were more influential. Female students more often described humiliation and emotional burden, whereas some male students framed negative feedback as a 'rite of passage.' Feedback experiences in transnational medical programs appear less determined by national culture than by early life experiences and hierarchical clinical environments. Hofstede's framework offered limited explanatory value, while Figured Worlds illuminated how identities are negotiated in feedback encounters. To enhance feedback cultures in transnational settings, faculty development should prioritise dialogic approaches, sensitivity to learners' prior experiences, and awareness of gendered impacts. Institutional change is needed to move beyond transmissive practices and foster learner-centred, inclusive feedback.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2567075"},"PeriodicalIF":3.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12576907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145402376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The GLASS tool: a new lens for evaluating generalist leadership competencies for medical education.","authors":"Youngjin Cho, Stefanie Carter, Charles Gullo, Kelly Quesnelle, John Szarek, Gabi N Waite, Amy J Prunuske","doi":"10.1080/10872981.2025.2576571","DOIUrl":"10.1080/10872981.2025.2576571","url":null,"abstract":"<p><p>Recent shifts in medical education have moved curricula away from siloed, discipline-based courses toward competency-focused, integrated models. Leading within a longitudinal, integrated curriculum requires a distinct set of leadership competencies. To address this need, medical education leaders across five institutions collaborated to develop a generalist leadership framework. Using a modified Delphi process, the team conducted literature reviews reviewing both published and gray literature and held monthly meetings over six months. This process resulted in the identification of core competencies, organized into three domains: Humility and Life-long Learning, Supporting Diverse Collaborations, and Holistic Stewardship of the Curriculum. A competency based self-assessment tool, Generalist Leadership Assessment for Skills and Strengths (GLASS) Tool, was then developed around this framework. The GLASS tool was piloted and validated in workshops at three national medical education conferences. Feedback from these sessions demonstrated strong support for the tool, with an average of 94% agreement on the relevance and clarity of the competencies. Participants used the tool to self-assess their leadership competencies, with higher self-ratings observed in the domain of humility and life-long learning over holistic stewardship of the curriculum. The tool was refined based on expert input clarifying the milestones and consolidating the competencies. The GLASS tool is designed to support reflective leadership development across four levels of educational engagement: individual, departmental, institutional, and national. It offers a transparent approach for faculty development, leadership readiness, and strategic planning in evolving medical education environments.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2576571"},"PeriodicalIF":3.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12604117/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145482839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medical students' perceptions of AI-based feedback and feedforward on communication skills in doctor-patient consultation - an acceptance study in a video-based simulation.","authors":"Moritz Bauermann, Thomas Rotthoff, Tobias Hallmen, Miriam Kunz, Elisabeth André, Ann-Kathrin Schindler","doi":"10.1080/10872981.2025.2592414","DOIUrl":"10.1080/10872981.2025.2592414","url":null,"abstract":"<p><p>Feedback and feedforward are highly relevant in promoting students' learning. With advances in artificial intelligence (AI), new opportunities to support feedback and feedforward are emerging. However, few studies have explored how medical students perceive and accept AI-based feedback and feedforward in medical communication training. In this study, we explored medical students' perceptions of AI-based and avatar-mediated feedback and feedforward in a simulation applying a doctor-patient consultation video. The participants comprised 82 medical students (56.1% female), 66 of whom were in their second semester and 16 in their fourth semester. Before participants saw a video of a medical student in a standardized pre-recorded consultation, they were asked to put themselves in the position of the peer shown. A human-like avatar subsequently provided AI-based feedback and feedforward for the medical student in the video. The participants-still taking on the shown medical student's role-were then asked to rate the perceived trustworthiness and their potential learning acceptance of the AI-based feedback and feedforward. The participants' ratings of trustworthiness and potential learning acceptance were higher for the AI-based, avatar-mediated feedforward than the feedback. Additionally, they reported a generally positive attitude toward AI. This attitude was positively correlated with a higher potential learning acceptance of feedback. The tendency to favor feedforward over feedback in interpersonal contexts-as described in the literature-was evident for the perception of the AI-based, avatar-mediated evaluations of a simulated doctor-patient consultation video. Future research could apply these insights to enhance AI-based learning in medical education, e.g. by providing students with AI-based feedforward on their own consultation videos and assessing their perceptions of the same.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2592414"},"PeriodicalIF":3.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12671429/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145649717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the impact of academic coaching interventions on student outcomes in graduate healthcare and medical education: a systematic scoping review.","authors":"Megan Donaldson, Sean McBride, Mark Wilhelm, Jennifer Parent-Nichols, René Thomas, Eileen Scanlon, David Griswold","doi":"10.1080/10872981.2025.2581671","DOIUrl":"10.1080/10872981.2025.2581671","url":null,"abstract":"<p><p>Academic coaching is an increasingly vital strategy in higher education, fostering skill development, academic resilience, and personal growth. While research suggests that coaching enhances performance and key competencies such as time management, stress reduction, and self-advocacy, systematic evaluations are needed to assess its effectiveness across graduate medical and healthcare education. This systematic scoping review investigates the effects of academic coaching interventions on academic and professional outcomes among graduate-level health and medical students. We conducted a comprehensive literature search and screening process following the PRISMA methodology and registered in the International Database of Education Systematic Reviews (IDESR #IDESR000171). Eleven studies met the inclusion criteria and were analyzed using the Population, Intervention, Comparison, and Outcomes (PICO) framework to contextualize findings. The review revealed notable variation in target populations, program structures, focus areas, and data collection methods. Medical student studies addressed both early and advanced learners, with interventions ranging from self-regulated learning and executive/life coaching to academic remediation and longitudinal coaching relationships. Tailored academic coaching interventions show promise in supporting learner development across a range of outcomes. However, the effectiveness of these programs depends on their alignment with learners' specific needs, educational stages, and institutional contexts. Future coaching initiatives should be designed with attention to the Master Adaptive Learner framework to ensure responsiveness to the evolving demands of health professions education.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2581671"},"PeriodicalIF":3.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12642885/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145574673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Token-splitting improves GPT-4.1 performance on plastic surgery exams: implications for AI-Assisted medical education.","authors":"Yung-Hsu Lei, Chien-Chung Chen, Ching-Ju Shen","doi":"10.1080/10872981.2025.2602788","DOIUrl":"10.1080/10872981.2025.2602788","url":null,"abstract":"<p><p>Large language models (LLMs), such as ChatGPT, have demonstrated impressive performance on general medical examinations; however, their effectiveness significantly declines in specialized board examinations due to limited domain-specific training data and computational constraints inherent to their self-attention mechanisms. This study investigates a novel token-splitting strategy informed by Cognitive Load Theory (CLT), aimed at overcoming these limitations by optimizing cognitive processing and enhancing knowledge retention in specialized educational contexts. We implemented a token-splitting approach by segmenting Taiwan plastic surgery board examination materials and associated textbook content into cognitively manageable segments ranging from 4,000 to 20,000 tokens. These segmented inputs were provided to GPT-4.1 via its standard ChatGPT web interface. Model performance was rigorously evaluated, comparing accuracy and efficiency across various token lengths and question complexities classified according to Bloom's taxonomy.The GPT-4.1 model utilizing the token-splitting strategy significantly outperformed the baseline (unmodified) model, achieving notably higher accuracy. The optimal segmentation length was determined to be 6,000 tokens, effectively balancing cognitive coherence with information retention and model attention. Errors observed at this optimal length primarily resulted from content absent from textual materials or requiring multimodal interpretation (e.g., image-based reasoning). Provided relevant textual content was adequately segmented, GPT-4.1 consistently demonstrated high accuracy (From 75.88% to 92.93%). The findings highlight that a token-splitting approach, grounded in Cognitive Load Theory, significantly enhances LLM performance on specialized medical board examinations. This accessible, user-friendly strategy provides educators and clinicians with a practical means to improve AI-assisted education outcomes without requiring complex technical skills or infrastructure. Future research and development integrating multimodal capabilities and adaptive segmentation strategies promise to further optimize educational applications and clinical decision-making support.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2602788"},"PeriodicalIF":3.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12704135/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145745190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MOOC-virtual simulation integration enhances surgical clinical skills: a quasi-experimental study.","authors":"Wang Zhang, Zonghuan Li, Pengcheng Li, Changhuan Liu, Zheng Wang, Yuping Liu, Lekai Zhu, Yulong Shi, Xue Fang, Xinghuan Wang, Zhe Xie, Xin Wang","doi":"10.1080/10872981.2025.2579396","DOIUrl":"10.1080/10872981.2025.2579396","url":null,"abstract":"<p><p>This study aimed to evaluate the impact of a teaching model combining massive open online course (MOOC) with virtual simulation experiments on clinical skills training. A quasi-experimental design was employed in the Second Clinical College of Wuhan University. The control group received traditional face-to-face teaching, while the experimental group received a teaching model combining MOOC instruction with virtual simulation experiments. After completing the course, both groups participated in a theoretical test and a Structured Clinical Skills Assessment to assess their mastery of clinical skills. Additionally, a questionnaire survey was conducted to assess the effectiveness of the experimental teaching model and to gauge student satisfaction. The experimental group's theoretical test scores were significantly higher than those of the control group, and the experimental group's Structured Clinical Skills Assessment scores were significantly higher than those of the control group, with statistically significant differences (<i>p</i> < 0.05). The post-course questionnaire survey of the experimental group showed that most students were satisfied with the teaching model. In the general surgery experiment course, the teaching model combining MOOC instruction with virtual simulation experiments is an effective teaching method that improves surgical skill performance and increases student satisfaction.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2579396"},"PeriodicalIF":4.4,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12587814/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145439662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When AI models take the exam: large language models vs medical students on multiple-choice course exams.","authors":"Pablo Ros-Arlanzón, Renato Gutarra-Ávila, Vicente Arrarte-Esteban, Vicente Bertomeu-González, Luis Hernández-Blasco, Mar Masiá, Laura Navarro-Canto, Juan Nieto-Navarro, Javier Abarca, Angel P Sempere","doi":"10.1080/10872981.2025.2592430","DOIUrl":"10.1080/10872981.2025.2592430","url":null,"abstract":"<p><p>Large language models (LLMs) are increasingly used in healthcare and medical education, but their performance on institution-authored multiple-choice questions (MCQs), particularly with negative marking, remains unclear. To compare the examination performance of five contemporary LLMs with enrolled medical students on final multiple-choice (MCQ-style) course exams across four clinical courses. We conducted a comparative cross-sectional study at Miguel Hernández University (Spain) in 2025. Final exams in Infectious Diseases, Neurology, Respiratory Medicine, and Cardiovascular Medicine were administered under routine conditions in Spanish. Five LLMs (OpenAI o1, GPT-4o, DeepSeek R1, Microsoft Copilot, and Google Gemini 1.5 Flash) completed all MCQs in two independent runs. Scores were averaged and test-retest was estimated with Gwet's AC1. Student scores (<i>n</i> = 442) were summarized as mean ± SD or median (IQR). Pairwise differences between models were explored with McNemar's test; student-LLM contrasts were descriptive. Across courses, LLMs consistently exceeded the student median and, in several instances, the highest student score. Mean LLM courses scores ranged 7.46-9.88, versus student means 4.28-7.32. OpenAI o1 achieved the highest mean in three courses; Copilot led in Cardiovascular Medicine (text-only subset due to image limitations). All LLMs answered every MCQ and short term test-retest agreement was high (AC1 0.79-1.00). Aggregated across courses, LLMs averaged 8.75 compared with 5.76 for students. On department-set Spanish MCQ exams with negative marking, LLMs outperformed enrolled medical students, answered every item, and showed high short-term reproducibility. These findings support cautious, faculty-supervised use of LLMs as adjuncts to MCQ assessment (e.g. automated pretesting, feedback). Confirmation across institutions, languages, and image-rich formats, and evaluation of educational impact beyond accuracy are needed.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2592430"},"PeriodicalIF":3.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12667333/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145641194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Twelve tips for developing and implementing AI curriculum for undergraduate medical education.","authors":"Do-Hwan Kim, Ye Ji Kang, Young-Mee Lee","doi":"10.1080/10872981.2025.2585637","DOIUrl":"10.1080/10872981.2025.2585637","url":null,"abstract":"<p><p>The rapid evolution of artificial intelligence (AI) and its growing role in clinical settings have made AI education a priority in undergraduate medical education. To support this, AI curricula must align with existing medical education frameworks while addressing AI's distinctive characteristics. This article outlines twelve actionable tips to guide the development and implementation of such curricula. These include defining the purpose and scope of AI education within the broader context of existing competency frameworks and digital health. The curriculum should be structured to allow for progressive deepening and integration of content, prioritizing key elements. Additionally, sustainable AI education depends on securing institutional resources, providing learners with authentic experiences, and ensuring continuous evaluation and improvement of the curriculum. Together, these approaches aim to help medical schools prepare students to practice effectively in a future where AI is a core component of medical practice.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2585637"},"PeriodicalIF":3.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12673982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145655875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Near-peer mentorship for newly qualified doctors; what are the benefits, and what methods can be used to overcome the pitfalls?","authors":"Patrick McGown, Jennifer Forshaw, Lisa Gould","doi":"10.1080/10872981.2025.2579079","DOIUrl":"10.1080/10872981.2025.2579079","url":null,"abstract":"<p><strong>Introduction: </strong>Access to mentorship is frequently cited as a priority for doctors, however formal mentorship programmes throughout training are lacking for residents. Near-peer mentorship is a faculty-light option to potentially bridge this mentorship gap, however the literature in the clinical postgraduate setting is not comprehensive. We aim to evaluate the benefits and pitfalls of near-peer mentorship in a postgraduate setting.</p><p><strong>Materials and methods: </strong>This study conducted a narrative review based on previously published frameworks. An exhaustive search of the literature was conducted using PubMed and OVID Medline. The findings were analysed using Framework synthesis, in which the pre-existing framework of <i>benefits</i> and <i>pitfalls</i> of near-peer mentorship were applied. After allocation to the frameworks' overarching themes, data was analysed thematically.</p><p><strong>Results: </strong>Across 10 identified studies (one quantitative, three qualitative, six mixed-methods), near-peer mentorship was perceived to be beneficial (72-100% approval), with 85-99% of mentors and mentees desiring continuation of schemes at their units (seven UK-based, two Australian, one Canadian). The main themes identified were Mentee benefits, including careers advice and development of transferable skills; Mentor benefits, including leadership and organisational skill improvements; Organisational benefits, including reduced faculty workload and an enhanced sense of community. Pitfalls included a perceived lack of mentor expertise, shortage of time and resources, and unsupportive mentoring relationships.</p><p><strong>Discussion: </strong>This research suggests that near-peer mentorship offers benefits for the mentee, mentor and organisation, if care is taken to mitigate the potential pitfalls. The main benefits versus traditional senior faculty mentorship derive from the concept of social and cognitive congruence, whereby a more closely relatable tutor is better able to tailor learning in terms understood by the tutee. Practical recommendations to optimise near-pear mentorship include mentorship pyramids, matching of pairings within the same clinical sites, and a hybrid matching approach with the option for mentee-led selection.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2579079"},"PeriodicalIF":3.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12604131/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145482475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors influencing the late submission of assignments in a medical university environment: a qualitative case study.","authors":"Mikio Hayashi, Rintaro Imafuku, Katsumi Nishiya","doi":"10.1080/10872981.2025.2569936","DOIUrl":"10.1080/10872981.2025.2569936","url":null,"abstract":"<p><p>The late submission of assignments by medical students is a chronic problem. Students' characteristics and past academic performances are useful indicators for predicting delays during medical school. However, medical students' perspectives on factors that lead to delays in assignment submission remain unexplored. To address this issue, this study examines the factors that influence the late submission of assignments in a medical university setting from the perspective of medical students. This exploratory qualitative case study recruited 20 undergraduate medical students who had submitted their assignments late at a private Japanese medical university. Data were collected through face-to-face, semi-structured interviews and analyzed using thematic analysis from a constructivist paradigm. A qualitative analysis revealed the following three themes regarding the factors influencing the late submission of assignments: (1) factors related to the medical university curriculum, including its structure, course offerings, and academic requirements, (2) factors related to the instructions from and authority of faculty, and (3) situational factors that exacerbate delays in student assignment submissions. Medical students felt that the nature of the assignments, which were assigned with no apparent regard for student schedules and offered a limited evaluative metric, was often irrational. The students implicitly accepted this situation while being aware of the infeasible submission deadlines. Medical students may have an internal sense of the perceived irrationality of faculty-imposed assignments and the authority behind them, and may strategically neglect some in favor of efficient course completion. Faculty members should ensure their assignments are consistent with course contents.</p>","PeriodicalId":47656,"journal":{"name":"Medical Education Online","volume":"30 1","pages":"2569936"},"PeriodicalIF":3.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12536618/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}