Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
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Natural language processing (NLP) tools including recently developed large language models (LLMs) have myriad potential applications in medical care and research, including the efficient labeling and classification of unstructured text such as electronic health record (EHR) notes. This opens the door to large-scale projects that rely on variables that are not typically recorded in a structured form, such as patient signs and symptoms. ⋯ The study demonstrated the utility of LLMs and transformer-based models in particular for extracting UTI symptoms from unstructured ED clinical notes; models were highly accurate for detecting the presence or absence of any UTI symptom on the note level, with variable performance for individual symptoms.
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Review Meta Analysis
Assessing the one-month mortality impact of civilian-setting prehospital transfusion: A systematic review and meta-analysis.
Based on convincing evidence for outcomes improvement in the military setting, the past decade has seen evaluation of prehospital transfusion (PHT) in the civilian emergency medical services (EMS) setting. Evidence synthesis has been challenging, due to study design variation with respect to both exposure (type of blood product administered) and outcome (endpoint definitions and timing). The goal of the current meta-analysis was to execute an overarching assessment of all civilian-arena randomized controlled trial (RCT) evidence focusing on administration of blood products compared to control of no blood products. ⋯ Current evidence does not demonstrate 1-month mortality benefit of civilian-setting PHT. This should give pause to EMS systems considering adoption of civilian-setting PHT programs. Further studies should not only focus on which formulations of blood products might improve outcomes but also focus on which patients are most likely to benefit from any form of civilian-setting PHT.