BACKGROUND: The National Medical Licensing Examination (NMLE) is the only objective, standardized metric to evaluate whether a medical student possessing the professional knowledge and skills necessary to work as a physician. However, the overall pass rate of NMLE in our hospital in 2021 was much lower than that of Peking Union Medical College Hospital, which was required to be further improved.
METHODS: To find the reasons for the unsatisfactory performance in 2021, the quality improvement team (QIT) organized regular face-to-face meetings for in-depth discussion and questionnaire, and analyzed the data by \"Plato analysis\" and \"Brainstorming method\". After finding out the reasons, the \"Plan-Do-Check-Action\" (PDCA) cycle was continued to identify and solve problems, which included the formulation and implementation of specific training plans by creating the \"Gantt charts\", the check of effects, and continuous improvements from 2021 to 2022. Detailed information about the performance of students in 2021 and 2022, and the attendance, assessment, evaluation and suggestions from our hospital were provided by the relevant departments, and the pass rate-associated data was collected online.
RESULTS: After the PDCA plan, the pass rate of NMLE in our hospital increased by 10.89% from 80.15% in 2021 to 91.04% in 2022 (P = 0.0109), with the pass rate of skill examination from 95.59% in 2021 to 99.25% in 2022 (P = 0.0581) and theoretical examination from 84.5% in 2021 to 93.13% in 2022 (P = 0.027). Additionally, the mean scores of all examinees increased with the theoretical examination score increasing from 377.0 ± 98.76 in 2021 to 407.6 ± 71.94 in 2022 (P = 0.004).
CONCLUSIONS: Our results showed a success application of the PDCA plan in our hospital which improved the pass rate of the NMLE in 2022, and the PDCA plan may provide a practical framework for future medical education and further improve the pass rate of NMLE in the next year.

OBJECTIVE: Students who earn their medical doctorate (MD) in the U.S. must pass the United States Medical Licensing Exam (USMLE) Step-1. The application process for students with disabilities who seek Step-1 accommodations can be arduous, barrier-ridden, and can impose a significant burden that may have long-lasting effects. We sought to understand the experiences of medical students with Type-1 Diabetes (T1D) who applied for Step-1 accommodations.
METHODS: A Qualtrics survey was administered to students enrolled in Liaison Committee on Medical Education (LCME)-accredited MD programs who disclosed having a primary diagnosis of T1D. Basic counts and qualitative inductive analyses were conducted.
RESULTS: Of the 21 surveys sent, 16 (76.2%) participants responded. Of the 16 respondents, 11 (68.8%) applied for USMLE Step-1 accommodations, whereas 5 (31.2%) did not. Of the 11 who applied for accommodations, 7 (63.6%) received the accommodations requested, while 4 (36.4%) did not. Of those who received the accommodations requested, 5/7 (71.4%) experienced at least one diabetes-related barrier on exam day. Of those who did not apply for Step-1 accommodations, 4/5 (80%) participants reported experiencing at least one diabetes-related barrier on exam day. Overall, 11/16 (68.8%) students experienced barriers on exam day with or without accommodations. Qualitative analysis revealed themes among participants about their experience with the process: frustration, anger, stress, and some areas of general satisfaction.
CONCLUSIONS: This study reports the perceptions of students with T1D about barriers and inequities in the Step-1 accommodations application process. Students with and without accommodations encountered T1D-related obstacles on test day.

ChatGPT has garnered attention as a multifaceted AI chatbot with potential applications in medicine. Despite intriguing preliminary findings in areas such as clinical management and patient education, there remains a substantial knowledge gap in comprehensively understanding the chances and limitations of ChatGPT\'s capabilities, especially in medical test-taking and education. A total of n = 2,729 USMLE Step 1 practice questions were extracted from the Amboss question bank. After excluding 352 image-based questions, a total of 2,377 text-based questions were further categorized and entered manually into ChatGPT, and its responses were recorded. ChatGPT\'s overall performance was analyzed based on question difficulty, category, and content with regards to specific signal words and phrases. ChatGPT achieved an overall accuracy rate of 55.8% in a total number of n = 2,377 USMLE Step 1 preparation questions obtained from the Amboss online question bank. It demonstrated a significant inverse correlation between question difficulty and performance with rs = -0.306; p < 0.001, maintaining comparable accuracy to the human user peer group across different levels of question difficulty. Notably, ChatGPT outperformed in serology-related questions (61.1% vs. 53.8%; p = 0.005) but struggled with ECG-related content (42.9% vs. 55.6%; p = 0.021). ChatGPT achieved statistically significant worse performances in pathophysiology-related question stems. (Signal phrase = \"what is the most likely/probable cause\"). ChatGPT performed consistent across various question categories and difficulty levels. These findings emphasize the need for further investigations to explore the potential and limitations of ChatGPT in medical examination and education.

BACKGROUND: The learning objectives in the current cross-sectional subject \"Rehabilitation, Physical Medicine, Naturopathic Medicine\" have been revised as part of the further development of the National Competency-Based Catalogue of Learning Objectives for Medicine (NKLM) to its new version 2.0. Since the NKLM is designed as an interdisciplinary catalogue, a subject assignment seemed necessary from the point of view of various stakeholders. Thus, the German Association of Scientific Medical Societies (AWMF) and the German medical faculties initiated a subject assignment process. The assignment process for the subject \"Physical and Rehabilitative Medicine, Naturopathic Medicine\" (PRM-NHV; according to the subject list of the first draft of the planned novel medical license regulations from 2020) is presented in this paper.
METHODS: The AWMF invited its member societies to participate in the assignment of learning objectives of chapters VI, VII, and VIII of the NKLM 2.0 to the individual subjects to which they consider to contribute in teaching. For \"PRM-NHV\", representatives of the societies for rehabilitation sciences (DGRW), physical and rehabilitation medicine (DGPRM), orthopaedics and traumatology (DGOU), as well as for naturopathy (DGNHK) participated. In a structured consensus process according to the DELPHI methodology, the learning objectives were selected and consented. Subsequently, subject recommendations were made by the AWMF for each learning objective.
RESULTS: From the NKLM 2.0, a total of 100 competency-based learning objectives of chapters VII and VIII for the subject \"PRM-NHV\" were consented by the representatives of the involved societies for presentation on the NKLM 2.0 online platform.
CONCLUSIONS: In the context of the revision process of medical studies in Germany and under the umbrella of the AWMF and the German medical faculties, a broad consensus of competency-based learning objectives in the subject \"PRM-NHV\" could be achieved. This provides an important orientation for all medical faculties both for the further development of teaching in the cross-sectional subject \"Rehabilitation, Physical Medicine, Naturopathic Medicine\" according to the 9th revision of the medical license regulations, which has been valid for twenty years, and for the preparation of the corresponding subjects in the draft bill of the novel license regulations.
UNASSIGNED: Im Rahmen der Weiterentwicklung des Nationalen Kompetenzbasierten Lernzielkatalogs (NKLM) zur Version 2.0 erfolgte auch eine Überarbeitung der Lernziele im bisherigen Querschnittsfach „Rehabilitation, Physikalische Medizin, Naturheilverfahren“. Da der NKLM grundsätzlich fächerübergreifend angelegt ist, von verschiedenen Seiten aber eine Fächerzuordnung notwendig schien, initiierten die Arbeitsgemeinschaft wissenschaftlich-medizinischer Fachgesellschaften (AWMF) und der Medizinische Fakultätentag einen Fächerzuordnungsprozess. Der Zuordnungsprozess für das Fach „Physikalische und Rehabilitative Medizin, Naturheilverfahren“ (PRM-NHV; laut Fächerliste des ersten Referentenentwurfes der geplanten Approbationsordnung aus dem Jahr 2020) wird in dieser Arbeit dargestellt.
UNASSIGNED: Die AWMF lud ihre Mitgliedsgesellschaften ein, sich an der Zuordnung von Lernzielen der Kapitel VI, VII und VIII des NKLM 2.0 zu den einzelnen Fächern zu beteiligen, zu denen sie nach eigener Einschätzung einen Beitrag in der Lehre leisten. Für „PRM-NHV“ beteiligten sich Vertreter*innen der DGRW, DGPRM, DGOU sowie der DGNHK. In einem strukturierten Konsensprozess nach der DELPHI-Methodik erfolgten Auswahl und Konsentierung der Lernziele. Anschließend erfolgte durch die AWMF eine Fächerempfehlung.
UNASSIGNED: Aus dem NKLM 2.0 wurden insgesamt 100 kompetenzbasierte Lernziele der Kapitel VII und VIII für das Fach „PRM-NHV“ von den Vertreter*innen der beteiligten Fachgesellschaften zur Darstellung auf der NKLM 2.0-Online-Plattform konsentiert.
UNASSIGNED: Im Rahmen des grundlegend geplanten Reformprozesses des Medizinstudiums und unter dem Dach der AWMF und des Medizinischen Fakultätentags gelang die breite Konsentierung von Lernzielen des NKLM 2.0 für die kompetenzbasierte Lehre im Fach „PRM-NHV“ durch die beteiligten Fachgesellschaften. Damit wird eine für alle medizinischen Fakultäten wichtige Orientierung sowohl für die Weiterentwicklung der Lehre im Querschnittsbereich „Rehabilitation, Physikalische Medizin, Naturheilverfahren“ gemäß der seit zwanzig Jahren gültigen 9. Revision der ÄApprO als auch für die Vorbereitung der entsprechenden Fächer im Referentenentwurf der neuen ÄApprO gegeben.

OBJECTIVE: This study assessed the content of US Medical Licensing Examination question banks with regard to out-of-hospital births and whether the questions aligned with current evidence.
METHODS: Three question banks were searched for key words regarding out-of-hospital births. A thematic analysis was then utilized to analyze the results.
RESULTS: Forty-seven questions were identified, and of these, 55% indicated a lack of inadequate, limited, or irregular prenatal care in the question stem.
CONCLUSIONS: Systematic studies comparing prenatal care in out-of-hospital births versus hospital births are nonexistent, leading to the potential for bias and adverse outcomes. Adjustments to question stems that accurately portray current evidence are recommended.

暂无摘要。

BACKGROUND: Two Jewish medical students who were forced to discontinue their study upon the raise of the Nazi regime, returned/ immigrated to Palestine and did their internship in Palestine. A third student, although faced with many procedural limitations, was able to continue most of his studies in Berlin including passing the MD examination. The first two students returned, after some years, to Berlin to sit for the Doctor examination which enabled them to gain a permanent medical license in Palestine. We describe the different backgrounds of the 3 students which enabled them to do the examination at Berlin\'s medical faculty during the Nazi regime. The follow up of the three, revealed glorious medical career during the British mandate and during the first years of the new state of Israel. The Dissertations were signed and supported by three leading Professors of the Berlin\'s Faculty. Two of them were found to have a National-Socialistic background.

BACKGROUND: Evaluation of students\' learning strategies can enhance academic support. Few studies have investigated differences in learning strategies between male and female students as well as their impact on United States Medical Licensing Examination® (USMLE) Step 1 and preclinical performance.
METHODS: The Learning and Study Strategies Inventory (LASSI) was administered to the classes of 2019-2024 (female (n = 350) and male (n = 262)). Students\' performance on preclinical first-year (M1) courses, preclinical second-year (M2) courses, and USMLE Step 1 was recorded. An independent t-test evaluated differences between females and males on each LASSI scale. A Pearson product moment correlation determined which LASSI scales correlated with preclinical performance and USMLE Step 1 examinations.
RESULTS: Of the 10 LASSI scales, Anxiety, Attention, Information Processing, Selecting Main Idea, Test Strategies and Using Academic Resources showed significant differences between genders. Females reported higher levels of Anxiety (p < 0.001), which significantly influenced their performance. While males and females scored similarly in Concentration, Motivation, and Time Management, these scales were significant predictors of performance variation in females. Test Strategies was the largest contributor to performance variation for all students, regardless of gender.
CONCLUSIONS: Gender differences in learning influence performance on STEP1. Consideration of this study\'s results will allow for targeted interventions for academic success.

暂无摘要。

Efforts are being made to improve the time effectiveness of healthcare providers. Artificial intelligence tools can help transcript and summarize physician-patient encounters and produce medical notes and medical recommendations. However, in addition to medical information, discussion between healthcare and patients includes small talk and other information irrelevant to medical concerns. As Large Language Models (LLMs) are predictive models building their response based on the words in the prompts, there is a risk that small talk and irrelevant information may alter the response and the suggestion given. Therefore, this study aims to investigate the impact of medical data mixed with small talk on the accuracy of medical advice provided by ChatGPT. USMLE step 3 questions were used as a model for relevant medical data. We use both multiple-choice and open-ended questions. First, we gathered small talk sentences from human participants using the Mechanical Turk platform. Second, both sets of USLME questions were arranged in a pattern where each sentence from the original questions was followed by a small talk sentence. ChatGPT 3.5 and 4 were asked to answer both sets of questions with and without the small talk sentences. Finally, a board-certified physician analyzed the answers by ChatGPT and compared them to the formal correct answer. The analysis results demonstrate that the ability of ChatGPT-3.5 to answer correctly was impaired when small talk was added to medical data (66.8% vs. 56.6%; p = 0.025). Specifically, for multiple-choice questions (72.1% vs. 68.9%; p = 0.67) and for the open questions (61.5% vs. 44.3%; p = 0.01), respectively. In contrast, small talk phrases did not impair ChatGPT-4 ability in both types of questions (83.6% and 66.2%, respectively). According to these results, ChatGPT-4 seems more accurate than the earlier 3.5 version, and it appears that small talk does not impair its capability to provide medical recommendations. Our results are an important first step in understanding the potential and limitations of utilizing ChatGPT and other LLMs for physician-patient interactions, which include casual conversations.