Context: The Liaison Committee on Medical Education (LCME) requires there to be: '...comparable educational experiences and equivalent methods of evaluation across all alternative instructional sites within a given discipline'. It is an LCME accreditation requirement that students encounter similar numbers of patients with similar diagnoses. However, previous empirical studies have not shown a correlation between the numbers of patients seen by students and performance on multiple-choice examinations.
Objective: This study examined whether student exposure to patients with specific diagnoses predicts performance on multiple-choice examination questions pertaining to those diagnoses.
Methods: The Department of Pediatrics at the University of Nebraska Medical Center has collected patient logbooks from clerks since 1994. These contain information on patient demographics and students' roles in patient care. During week 7 of an 8-week course, students took an examination intended to help them prepare for their final examination. Logbooks and pre-examination questions were coded using standard ICD-9 codes. Data were analysed using Minitab statistical software to determine dependence between patient encounters and test scores. Subjects comprised a convenience sample of students who completed the clerkship during 1997-2000.
Results: Our analysis indicates that performance on a multiple-choice examination is independent of the number of patients seen.
Conclusions: Our data suggest knowledge-based examination performance cannot be predicted by the volume of patients seen. Therefore, too much emphasis on examination performance in clinical courses should be carefully weighed against clinical performance to determine the successful completion of clerkships.
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http://dx.doi.org/10.1111/j.1365-2923.2007.02764.x | DOI Listing |
J Dent Sci
January 2025
Division of Physiology, Department of Health Promotion, Kyushu Dental University, Kitakyushu, Japan.
Background/purpose: OpenAI's GPT-4V and Google's Gemini Pro, being Large Language Models (LLMs) equipped with image recognition capabilities, have the potential to be utilized in future medical diagnosis and treatment, ands serve as valuable educational support tools for students. This study compared and evaluated the image recognition capabilities of GPT-4V and Gemini Pro using questions from the Japanese National Dental Examination (JNDE) to investigate their potential as educational support tools.
Materials And Methods: We analyzed 160 questions from the 116th JNDE, administered in March 2023, using ChatGPT-4V, and Gemini Pro, which have image recognition functions.
J Multidiscip Healthc
January 2025
School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Objective: Common examinations for diagnosing obstructive sleep apnea (OSA) are polysomnography (PSG) and home sleep apnea testing (HSAT). However, both PSG and HSAT require that sensors be attached to a subject, which may disturb their sleep and affect the results. Hence, in this study, we aimed to verify a wireless radar framework combined with deep learning techniques to screen for the risk of OSA in home-based environments.
View Article and Find Full Text PDFAm J Ophthalmol Case Rep
March 2025
University of Florida, Department of Ophthalmology, USA.
Purpose: Human amniotic membrane (hAM) grafts have been used to close persistent macular holes in recent years. The results from these surgeries are promising with improved closure rate and vision. However, there is lack of data for what happens to these membranes and how long the tissue should remain inside the patient's eyes.
View Article and Find Full Text PDFBio Protoc
January 2025
Department of Stomatology, Peking Union Medical College Hospital, Beijing, China.
Pulpitis is an important and prevalent disease within the oral cavity. Thus, animal models are necessary tools for basic research focused on pulpitis. Researchers worldwide often use dogs and miniature pigs to construct animal models of pulpitis.
View Article and Find Full Text PDFCJC Open
January 2025
University Clinical Center of Serbia, Emergency Hospital, Cardiology Intensive Care Unit & Cardiology Clinic, Belgrade, Serbia.
Background: Insulin- and non-insulin treated diabetes (ITDM and NITDM) have different prognostic impact in patients with myocardial infarction and/or heart failure. The aim of this study was to analyze the prognostic impact of ITDM and NTIDM on the incidence of all-cause mortality and major adverse cardiovascular events (MACE- cardiovascular death, nonfatal infarction, nonfatal stroke, and target vessel revascularization) in the 8-year follow-up of patients with ST-segment elevation myocardial infarction (STEMI) with a reduced ejection fraction (EF).
Methods: We analyzed 2230 consecutive STEMI patients treated with primary percutaneous coronary intervention and with EF < 50%.
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