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The study, conducted by researchers and clinicians from Harvard Medical School, Massachusetts General Hospital, and Brigham and Women's Hospital, found that a large-language model (LLM) was able to extract presenting signs and symptoms of sepsis from the admission notes of more than 93,000 patients with accuracy that was equal to that of physicians performing a manual medical review. Signs, symptoms, and superbugs The researchers also examined the associations between presenting signs and symptoms and isolation of methicillin-resistant Staphylococcus aureus (MRSA) from a clinical culture within 72 hours of emergency department (ED) arrival, isolation of a multidrug-resistant gram-negative (MDRGN) organism, and in-hospital mortality. Analysis of the 30 most common sepsis signs and symptoms identified by the LLM produced seven syndromes corresponding to four sites of infection (skin and other soft tissue, cardiopulmonary, gastrointestinal, and urinary tract) that were directly correlated with ECD-10-CM discharge diagnosis codes that corresponded to infections at those sites. In a commentary published in the same journal, Jonathan Baghdadi, MD, PhD, of the University of Maryland School of Medicine, and Cristina Vazquez-Guillamet, MD, of the Washington University in St. Louis School of Medicine, say that although a tool that discerns the signs and symptoms of sepsis could be useful for understanding optimal approaches to early sepsis care," the LLM validated in the study is, at the moment, probably "better suited to automating simple tasks, such as the extraction of signs and symptoms, than participating in clinical decision-making.""