Predictive Modeling to Support Student Success on Step 2 Clinical Knowledge: A Multi-Cohort Model [0.03%]
预测建模以支持临床知识第二步考试成功的多队列模型
Phuong B Huynh,Heather E Harrell,Shelley Wells Collins et al.
Phuong B Huynh et al.
The shift of USMLE Step 1 to pass/fail redirected residency programs' attention to Step 2 Clinical Knowledge (CK), creating demand for accurate early‑warning tools. Develop and externally validate a concise model that predicts Step 2 CK pe...
Uncovering Gaps in Obesity Medicine Competencies: Insights from Ten U.S. Medical Schools [0.03%]
美国十所医学院在肥胖医学能力上的差距及启示
Magdalena Pasarica,Robert F Kushner,Angelina V Leary et al.
Magdalena Pasarica et al.
Competency-based obesity medicine education is critically needed in undergraduate medical training, as emphasized by the Association of American Medical Colleges. Using a competency-based framework, we identified key gaps in obesity educati...
"Remodeling Education in Pathology and Biomedical Sciences: Prompting Effective Learning and Teaching with AI LLMs" [0.03%]
“病理学和生物医学科学教育的重塑：利用AI大语言模型促进有效教学与学习”
Deilson Elgui de Oliveira
Deilson Elgui de Oliveira
Mastering pathology and other biomedical science disciplines requires integrating complex biomedical concepts. The emergence of Generative Artificial Intelligence (GenAI), based on large language models (LLMs), presents a valuable opportuni...
Student-specific Factors Associated with Passing the USMLE Step 1 Examination Within an Institution-allotted Dedicated Study Period [0.03%]
一所学校规定的专门学习期间影响USMLE第一部分考试通过的个人因素
Eva Spier,Emily Yamron,Bailey A Frohlich et al.
Eva Spier et al.
Background: In 2022, USMLE Step 1 scoring was changed from numeric to pass/fail. The pass rate dropped from 95% in 2021 to 90% in 2023 among US allopathic MD students. Predictors for student success and how study habits h...
A Framework for Designing Valid Manikin-Based Summative Assessments in Undergraduate Medical Education [0.03%]
基于模型的本科生医学教育总结性评估设计框架
Jannet Lee-Jayaram,Alexander J Bos,Dennis Bolger Jr et al.
Jannet Lee-Jayaram et al.
Manikin-based simulation is well established for formative use in undergraduate medical education, but its role in summative assessment is less defined, particularly in pre-clerkship learners. We describe a three-phase framework for develop...
Impact of Self-regulated Learning and Participation in Academic Support Programs on Performance of Pre-matriculation Medical Learners [0.03%]
自我调节学习和参与学业支持项目对预入学者成绩的影响
James Hadsell,Joshua Bernstein
James Hadsell
Purpose: Medical schools implement pre-matriculation programs to assist at-risk students develop essential skills required to succeed academically. Pre-matriculation programs integrate academic success and support activit...
Enhancing Ophthalmology Education in Medical Schools: Learned Lessons from Medical-Optometry Interprofessional Education Curriculum [0.03%]
医学教育中的眼科学教育增强：医学-验光学专业间教育课程的经验教训
Kareena Chawla,Taline Nazarian,Lauren Fine et al.
Kareena Chawla et al.
Ophthalmology education in U.S. medical schools has steadily declined, despite the prevalence of vision related complaints in clinical practice. To address this gap, Nova Southeastern University College of Allopathic Medicine implemented an...
Joshua A Roshal,Caitlin Silvestri,Tejas Sathe et al.
Joshua A Roshal et al.
[This retracts the article DOI: 10.1007/s40670-025-02352-5.]. © The Author(s) under exclusive licence to International Association of Medical Science Ed...
Investigating the Impact of Item Writing Flaws on Student Performance in Physiology Assessments at Two Institutions [0.03%]
两项研究机构生理学评估中项目编写缺陷对学生表现影响的调查分析
Gloria Urrutia,Larissa Dixon,Arielle Patterson et al.
Gloria Urrutia et al.
Graduate and professional students are frequently evaluated through multiple-choice question (MCQ) examinations; however, MCQs may present challenges for test-takers when they contain item writing flaws (IWFs). IWFs potentially compromise t...
AI for Assessment in Medical Education in Post LLM Era: A Scoping Review [0.03%]
后LLM时代医学教育中评估的人工智能研究：一项范围 reviews
Puneet Agarwal,Renu Agarwal,Igor Iezhitsa
Puneet Agarwal
Artificial intelligence (AI) has supported assessment in medical education for decades through automatic item generation and natural language processing (NLP), but these pre-large language model (LLM) approaches were narrow, tool-intensive,...