Objectives: In undergraduate medical education virtual patients (VPs) are a suitable method to teach clinical reasoning and support the visualization of this thinking process in a safe environment. The aim of our study was to investigate differences in the clinical reasoning process and diagnostic accuracy of female and male medical students.
Methods: During the summer term 2020, we provided access to 15 VPs for undergraduate students enrolled in a medical school in Bavaria, Germany. All interactions of the 179 learners within the VP system CASUS were recorded, exported, and analyzed.
Results: We found significant differences in the clinical reasoning of female and male learners. Female students documented more findings, differential diagnoses, tests, and treatment options and more often created a summary statement about the VP. Their overall performance was higher than those of their male peers, but we did not see any significant differences in diagnostic accuracy.
Conclusions: The significant differences between male and female medical students should be considered when planning teaching and research activities. A future study should investigate whether these differences can also be found in physicians.
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http://dx.doi.org/10.1515/dx-2022-0081 | DOI Listing |
Curr Cardiol Rep
January 2025
Department of Medical Imaging, Montreal Heart Institute, Montréal, Québec, Canada.
Purpose Of Review: This review aims to explore the clinical significance of atrial fluorodeoxyglucose (FDG) uptake observed in positron emission tomography (PET) scans, focusing on its association with atrial fibrillation (AF), cardiac sarcoidosis, and myocarditis. We discuss the implications of atrial uptake for patient management and prognosis.
Recent Findings: Recent studies have demonstrated that atrial FDG uptake is frequently present in patients with AF, particularly those with persistent AF, and is linked to increased risks of stroke and poorer outcomes after ablation.
J Am Med Inform Assoc
January 2025
Sinclair School of Nursing, University of Missouri, Columbia, MO 65211, United States.
Objective: This study aimed to explore the utilization of a fine-tuned language model to extract expressions related to the Age-Friendly Health Systems 4M Framework (What Matters, Medication, Mentation, and Mobility) from nursing home worker text messages, deploy automated mapping of these expressions to a taxonomy, and explore the created expressions and relationships.
Materials And Methods: The dataset included 21 357 text messages from healthcare workers in 12 Missouri nursing homes. A sample of 860 messages was annotated by clinical experts to form a "Gold Standard" dataset.
Asian J Med Humanit
January 2024
Faculté de Medicine, Université de Montréal, Montréal, Québec, Canada.
Objectives: The overall goal of this article is to show that denial is one of the greatest obstacles to good practical judgment and is therefore a major problem in clinical ethics by examining its cognitive structure and the challenges it poses for clinical ethics consultation and intervention. In addition to clinical examples, excerpts of verbatim from citizen forums on triage protocols will be used to illustrate the manifestations of denial in citizens when faced with difficult choices.
Case Presentation: The initial waves of the pandemic and the alarming resurgence of cases with the emergence of highly transmissible variants have created increased pressure on many healthcare systems around the world.
BMC Med Educ
January 2025
Riphah international university, Rawalpindi, Pakistan.
Background: Reflection fosters self-regulated learning by enabling learners to critically evaluate their performance, identify gaps, and make plans to improve. Feedback, in turn, provides external insights that complement reflection, helping learners recognize their strengths and weaknesses, adjust their learning strategies, and enhance clinical reasoning and decision-making skills. However, reflection alone may not produce the desirable effects unless coupled with feedback.
View Article and Find Full Text PDFNat Commun
January 2025
Institute of NeuroScience, Université catholique de Louvain, Brussels, Belgium.
Large Language Models have demonstrated expert-level accuracy on medical board examinations, suggesting potential for clinical decision support systems. However, their metacognitive abilities, crucial for medical decision-making, remain largely unexplored. To address this gap, we developed MetaMedQA, a benchmark incorporating confidence scores and metacognitive tasks into multiple-choice medical questions.
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