Clinical reasoning is one of the central competencies in everyday clinical practice. Diagnostic competence is often measured based on diagnostic accuracy. It is implicitly assumed that a correct diagnosis is based on a proper diagnostic process, although this has never been empirically tested. The frequency and nature of errors in students' diagnostic processes in correctly solved cases was analyzed in this study. 148 medical students processed 15 virtual patient cases in internal medicine. After each case, they were asked to state their final diagnosis and justify it. These explanations were qualitatively analyzed and assigned to one of the following three categories: correct explanation, incorrect explanation and diagnosis guessed right. The correct diagnosis was made 1,135 times out of 2,080 diagnostic processes. The analysis of the associated diagnostic explanations showed that 92% (1,042) reasoning processes were correct, 7% (80) were incorrect, and 1% (13) of the diagnoses were guessed right. Causes of incorrect diagnostic processes were primarily a lack of pathophysiological knowledge (50%) and a lack of diagnostic skills (30%). Generally, if the diagnosis is correct, the diagnostic process is also correct. The rate of guessed diagnoses is quite low at 1%. Nevertheless, about every 14th correct diagnosis is based on a false diagnostic explanation and thus, a wrong diagnostic process. To assess the diagnostic competence, both the diagnosis result and the diagnostic process should be recorded.
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http://dx.doi.org/10.3205/zma001293 | DOI Listing |
JAMA Netw Open
January 2025
Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, Ontario, Canada.
Importance: There have been limited evaluations of the patients treated at academic and community hospitals. Understanding differences between academic and community hospitals has relevance for the design of clinical models of care, remuneration for clinical services, and health professional training programs.
Objective: To evaluate differences in complexity and clinical outcomes between patients admitted to general medical wards at academic and community hospitals.
Curr Pain Headache Rep
January 2025
Department of Neurology, Danish Headache Center, Copenhagen University Hospital - Rigshospitalet, Valdemar Hansens Vej 5, Entrance 1A, 2600 Glostrup, Copenhagen, Denmark.
Purpose Of Review: To evaluate existing functional magnetic resonance imaging (fMRI) studies on post-traumatic headache (PTH) following traumatic brain injury (TBI).
Recent Findings: We conducted a systematic search of PubMed and Embase databases from inception to February 1, 2024. Eligible fMRI studies were required to include adult participants diagnosed with acute or persistent PTH post-TBI in accordance with any edition of the International Classification of Headache Disorders.
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.
Insights Imaging
January 2025
Department of Radiology, West China Hospital, Sichuan University, Chengdu, China.
Objectives: Renal cell carcinoma (RCC) with extrarenal fat (perinephric or renal sinus fat) invasion is the main evidence for the T3a stage. Currently, computed tomography (CT) is still the primary modality for staging RCC. This study aims to determine the diagnostic performance of CT in RCC patients with extrarenal fat invasion.
View Article and Find Full Text PDFEur Radiol Exp
January 2025
St Vincent's University Hospital, Dublin, Ireland.
Background: The large language model ChatGPT can now accept image input with the GPT4-vision (GPT4V) version. We aimed to compare the performance of GPT4V to pretrained U-Net and vision transformer (ViT) models for the identification of the progression of multiple sclerosis (MS) on magnetic resonance imaging (MRI).
Methods: Paired coregistered MR images with and without progression were provided as input to ChatGPT4V in a zero-shot experiment to identify radiologic progression.
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