Background: Physicians may receive diagnostic information in different orders, and there is a lack of empirical evidence that the order of presentation may influence clinical reasoning.
Objective: We investigated whether diagnostic accuracy of chest pain cases is influenced by the order of presentation of the history and electrocardiogram (EKG) to cardiology residents.
Methods: We conducted an experimental study during a resident training in 2019. Twelve clinical cases were presented in 2 diagnostic rounds. Residents were randomly allocated to seeing the EKG first (EKGF) or the history first (HF). The mean diagnostic accuracy scores (range 0-1) and confidence level (0-100) in each diagnostic round and time needed to make the diagnosis were evaluated.
Results: The final diagnostic accuracy was higher than the initial in both groups. After the first round, diagnostic accuracy was higher in HF (n=24) than in EKGF (n=28). Time taken to judge the history was comparable in both groups. Time taken to judge the EKG was shorter in HF (40±11 vs 64±13 seconds; <.01). Time invested in the second round was significantly correlated with changing the initial diagnosis. A significant difference was observed in confidence ratings after the initial diagnosis, with EKGF reporting less confidence relative to HF.
Conclusions: The order in which history and EKG are presented influences the clinical reasoning process.
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http://dx.doi.org/10.4300/JGME-D-21-01053.1 | DOI Listing |
Cancer Invest
January 2025
Department of Pathology & Laboratory Medicine, Aga Khan University, Karachi, Pakistan.
Accurate and timely diagnosis of t(9;22)-positive leukemias is vital to improving survival in pediatric patients. In low-resource settings, where healthcare disparities are exacerbated by limited resources, cost-effective and efficient diagnostic methods are essential for bridging these gaps and ensuring better outcomes. Among the diagnostic tools evaluated among 23 patients sample, RT-PCR demonstrated superior sensitivity (100%) and the shortest turnaround time (7 days), significantly outperforming FISH and karyotyping in both accuracy and timeliness.
View Article and Find Full Text PDFEmerg Microbes Infect
January 2025
Key Laboratory of Jiangxi Province for Transfusion Medicine, Department of Blood Transfusion, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, China.
The tRNA-derived small RNAs (tsRNAs) are a new class of non coding RNAs, which are stable in body fluids and can be used as potential biomarkers for disease diagnosis. However, the exact value of tsRNAs in the diagnosis of tuberculosis (TB) is still unclear. The objective of the present study was to evaluate the performance of the serum tsRNAs biosignature to distinguish between active TB, healthy controls, latent TB infection, and other respiratory diseases.
View Article and Find Full Text PDFDespite an increasing number of studies examining the effect of Single-Photon Emission Computed Tomography/ Computed Tomography (SPECT/CT) on improvement of diagnosis of aseptic loosening, there is still a great deal of uncertainty regarding its applicability in diagnostic algorithm. Therefore, in this meta-analysis, we aimed to investigate the diagnostic performance of SPECT/CT for identification of aseptic loosening in patients with persistent pain following the total knee arthroplasty (TKA) and total hip arthroplasty (THA). Electronic databases including Medline, Scopus, Web of Science, Cochrane library, and Embase were systematically searched for identifying relevant published studies from their inception to April 2023.
View Article and Find Full Text PDFWorld J Gastrointest Endosc
January 2025
Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China.
Background: Recent advancements in artificial intelligence (AI) have significantly enhanced the capabilities of endoscopic-assisted diagnosis for gastrointestinal diseases. AI has shown great promise in clinical practice, particularly for diagnostic support, offering real-time insights into complex conditions such as esophageal squamous cell carcinoma.
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Front Public Health
January 2025
Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
Introduction: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).
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