Background: One of the most important challenges in medical education is the preparation of multiple-choice questions able to discriminate between students with different academic level. Average questions may be very easy for students with good performance, reducing their discriminant power in this group of students. The aim of this study was to analyze if the discriminative power of multiple-choice questions is different according to the students' academic performance.
Methods: We retrospectively analyzed the difficulty and discrimination indices of 257 multiple-choice questions used for the end of course examination of pathophysiology and analyzed whether the discrimination indices were lower in students with good academic performance (group 1) than in students with moderate/poor academic performance (group 2). We also evaluated whether case-based questions maintained their discriminant power better than factual questions in both groups of students or not. Comparison of the difficulty and discrimination indices between both groups was based on the Wilcoxon test.
Results: Difficulty index was significantly higher in group 1 (median: 0.78 versus 0.56; P < 0.001) and discrimination index was significantly higher in group 2 (median: 0.21 versus 0.28; P < 0.001). Factual questions had higher discriminative indices in group 2 than in group 1 (median: 0.28 versus 0.20; P < 0.001), but discriminative indices of case-based questions did not differ significantly between groups (median: 0.30 versus 0.24; P = 0.296).
Conclusions: Multiple-choice question exams have lower discriminative power in the group of students with high scores. The use of clinical vignettes may allow to maintain the discriminative power of multiple-choice questions.
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http://dx.doi.org/10.1186/s12909-022-03844-3 | DOI Listing |
J Endod
January 2025
Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, Jazan University, Jazan, Saudi Arabia.
Introduction: Artificial intelligence models have shown potential as educational tools in healthcare, such as answering exam questions. This study aimed to assess the performance of four prominent chatbots: ChatGPT-4o, MedGebra GPT4o, Meta LIama 3, and Gemini Advanced in answering multiple-choice questions (MCQs) in endodontics.
Methods: The study utilized 100 MCQs each with 4 potential answers.
Lakartidningen
January 2025
professor, universitetssjukhusöverläkare, Sahlgrenska universitetssjukhuset, Göteborg; Sahlgrenska akademin, Göteborgs universitet.
Several of the requirements for obtaining a medical degree according to the Swedish Higher Education Ordinance illustrate the scientific basis of the profession, and systematic reviews as well as health technology assessments (HTA) constitute cornerstones in evidence-based medicine. In this study, medical students' experience of scientific education related to the profession was explored, and their knowledge achieved was sampled by five multiple-choice questions (MCQ). A total of 433 out of 641 students attending the final semester in six medical schools in Sweden participated (response rate: 68%).
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.
View Article and Find Full Text PDFBMJ Open
December 2024
Department of Microbiology and Mycology, National Institute of Preventive and Social Medicine, Dhaka, Bangladesh.
Background: Antimicrobial resistance (AMR) is a global health and development concern. Antimicrobial misuse and overuse are key contributors to the emergence of drug-resistant infections.
Objective: The current study aimed to determine the level of perception and practices of physicians regarding AMR in a tertiary-level hospital.
Crit Care Explor
January 2025
Department of Critical Care Medicine, Alberta Health Services, AB, Canada.
Importance: Nursing workforce changes, knowledge translation gaps, and environmental/organizational barriers may impact sepsis recognition and management within the ICU.
Objectives: To: 1) evaluate current ICU nursing knowledge of sepsis recognition and management, 2) explore individual and environmental or organizational factors impacting nursing recognition and management of sepsis using the Theoretical Domains Framework (TDF), and 3) describe perceived barriers and facilitators to nursing recognition and management of patients with sepsis.
Design, Setting, And Participants: This cross-sectional survey was administered to nurses working in four general system ICUs between October 24, 2023, and January 30, 2024.
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