Introduction: Large language models (LLMs) have grown in popularity in recent months and have demonstrated advanced clinical reasoning ability. Given the need to prioritize the sickest patients requesting emergency medical services (EMS), we attempted to identify if an LLM could accurately triage ambulance requests using real-world data from a major metropolitan area.
Methods: An LLM (ChatGPT 4o Mini, Open AI, San Francisco, CA, USA) with no prior task-specific training was given real ambulance requests from a major metropolitan city in the United States.
The spectrum of kidney disease among human immunodeficiency virus (HIV) infected patients is extensive. We describe a young male who was recently detected with HIV infection and antineutrophil cytoplasmic antibody (ANCA) negative pauci-immune crescentic glomerulonephritis. The patient had no extrarenal vasculitis involvement.
View Article and Find Full Text PDFPurpose: The purpose of this study was to review the currently available systematic reviews and meta-analyses comparing kinematic alignment (KA) and mechanical alignment (MA).
Methods: A literature search was performed to obtain all systematic review and meta-analyses comparing KA to MA that included one or more randomised controlled trials. A total of 18 studies were obtained, three of which were systematic reviews without meta-analysis.
Introduction: There is significant public health interest towards providing medical care at mass-gathering events. Furthermore, mass gatherings have the potential to have a detrimental impact on the availability of already-limited municipal Emergency Medical Services (EMS) resources. This study presents a cross-sectional descriptive analysis to report broad trends regarding patients who were transported from National Collegiate Athletic Association (NCAA) Division 1 collegiate football games at a major public university in order to better inform emergency preparedness and resource planning for mass gatherings.
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