Observational studyBrazilian journal of anesthesiology (Elsevier)
Evaluation of two large language models for intensive care unit discharge decisions: a prospective observational cohort study.
Observational study in Brazilian journal of anesthesiology (Elsevier). The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06584890 (The Evaluation of the Effectiveness of General Artificial Intelligence Models in Extubation Decision-Making in the Intensive Care Unit), which is not on this map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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The Evaluation of the Effectiveness of General Artificial Intelligence Models in Extubation Decision-Making in the Intensive Care Unit
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of this study was to evaluate the effectiveness of two general-purpose Large Language Models (LLMs), ChatGPT and Gemini, in predicting Intensive Care Unit (ICU) discharge decisions (discharge vs. non-discharge). By comparing their outputs with decisions made by ICU physicians, we sought to determine the alignment of AI-generated recommendations with expert clinical judgment and assess their potential as decision-support tools in critical care.
methodsThis prospective observational cohort study was conducted in a tertiary ICU between September 2024 and May 2025. Adult patients (≥ 18 years) requiring ICU discharge decisions were included. Standardized clinical prompts were generated from electronic health records and input into ChatGPT and Gemini. The models' binary discharge decisions were compared to those of ICU physicians. Model performance was assessed using accuracy, sensitivity, specificity, F1 score, Cohen's kappa, and McNemar's test. Discharge was defined as the positive class for all diagnostic performance analyses.
resultsA total of 398 patients were analyzed. ChatGPT demonstrated higher accuracy than Gemini (87.2% vs. 66.3%), with higher sensitivity (85.9% vs. 46.9%) and F1 score (0.890 vs. 0.628), whereas Gemini showed higher specificity (96.2% vs. 89.2%). Agreement with clinician decisions was substantial for ChatGPT (κ = 0.737, p = 0.024) and fair for Gemini (κ = 0.379, p < 0.001). Laboratory markers such as lactate, hemoglobin, and procalcitonin significantly differed between discharged and non-discharged patients.
conclusionLarge language models may support ICU discharge decisions when guided by structured, guideline-informed prompting. ChatGPT achieved higher overall accuracy, sensitivity, and F1 score, whereas Gemini demonstrated higher specificity.
trial registrationExternation (Discharge) of ICU, NCT06584890, registered 03 September 2024, prospectively registered, https://register. CLINICALTRIALS: gov/prs/beta/studies/S000EVXZ00000029/recordSummary.
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