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Objective: Orthopedic residents are tasked with rapidly acquiring clinical and surgical skills, especially during their PGY-1 year. However, resource constraints and other factors frequently cause skills training to fall short of established guidelines. We aimed to design and evaluate a cross-institutional, month-long curriculum aimed at pooling resources to optimize training.

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Objective: Discussions related to the importance of seeking specific consent for sensitive (e.g., pelvic, rectal) exams performed on anesthetized patients by medical students have been growing.

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Who is coming in? Evaluation of physician performance within multi-physician emergency departments.

Am J Emerg Med

January 2025

Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale University, New Haven, CT, USA.

Background: This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.

Methods: A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables.

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National early warning score 2 plus non-invasive capnography and perfusion index to estimate poor outcomes in emergency departments.

Am J Emerg Med

January 2025

Faculty of Medicine, Universidad de Valladolid, Valladolid, Spain; Emergency Department, Hospital Clínico Universitario, Gerencia Regional de Salud de Castilla y León, Valladolid, Spain.

Background: The study of the inclusion of new variables in already existing early warning scores is a growing field. The aim of this work was to determine how capnometry measurements, in the form of end-tidal CO2 (ETCO2) and the perfusion index (PI), could improve the National Early Warning Score (NEWS2).

Methods: A secondary, prospective, multicenter, cohort study was undertaken in adult patients with unselected acute diseases who needed continuous monitoring in the emergency department (ED), involving two tertiary hospitals in Spain from October 1, 2022, to June 30, 2023.

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This paper systematically evaluates saliency methods as explainability tools for convolutional neural networks trained to diagnose glaucoma using simplified eye fundus images that contain only disc and cup outlines. These simplified images, a methodological novelty, were used to relate features highlighted in the saliency maps to the geometrical clues that experts consider in glaucoma diagnosis. Despite their simplicity, these images retained sufficient information for accurate classification, with balanced accuracies ranging from 0.

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