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Войти в исследования BMC emergency medicine · 2026
Performance of GPT-5.5 thinking for Manchester Triage System-based emergency department triage: agreement with an expert reference standard and prediction of short-term outcomes.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Fettahoglu Sureyya Tuba, Az Adem, Sogut Ozgur, Akdemir Tarik, Ozden Muhammed Furkan, Fettahoglu Salih, Helvacı Mucahide Samli, Sisman Enes, Ozcomlekci Mehmet, Bilici Emir, Kilic Nurbaki, Saricicek Tugay, Celik Ayse Feyza, Colak Zekeriya, Eralp Zulkuf
Аннотация
OBJECTIVE: To evaluate the agreement between Manchester Triage System (MTS) category assignments generated by GPT-5.5 Thinking and an expert-derived reference standard in a selected cohort of emergency department (ED) patients, and to assess the model's ability to predict hospital admission and 72-hour mortality.
METHODS: This single-center observational study prospectively sampled consecutive ED presentations during a prespecified continuous 72-hour window, with triage-time variables and secondary outcomes obtained from hospital records. Patients classified as MTS Category 5 (Blue; nonurgent) by the expert reference standard were excluded from the analytic cohort. GPT-5.5 Thinking was prompted in a standardized single-shot format using structured clinical vignettes derived from triage data. The primary endpoint was agreement between model-assigned MTS categories and an expert reference standard established by two independent blinded emergency medicine specialist rater teams, with disagreements resolved through senior adjudication. Exact agreement, within-1-level agreement, overtriage, undertriage, and dangerous undertriage were calculated. Secondary analyses evaluated prediction of hospital admission and 72-hour mortality using diagnostic performance metrics.
RESULTS: A total of 1,841 patients were included. Overall agreement between GPT-5.5 Thinking and the expert reference standard was substantial (κ = 0.721; 95% CI 0.691-0.740). Exact agreement rate was 62.0%, and within-1-level agreement rate was 99.0%. Overtriage occurred in 35.3% of cases, undertriage in 2.7%, and dangerous undertriage in 0.2%. Exact agreement rate was significantly lower in patients aged ≥ 65 years than in younger adults (57.1% vs. 64.2%, p = 0.013). For hospital admission, accuracy rate was 79.9%, sensitivity 83.8%, positive predictive value (PPV) 45.2%, and negative predictive value (NPV) 95.9%. For 72-hour mortality, accuracy rate was 86.5%, sensitivity 72.3%, PPV 12.6%, and NPV 99.1%.
CONCLUSION: Within this selected ED cohort, GPT-5.5 Thinking showed substantial agreement with expert MTS-based ED triage and generally preserved the ordinal hierarchy of clinical urgency. Its performance was characterized by frequent adjacent-level agreement, predominant overtriage, and very rare but nonzero dangerous undertriage. The model also showed stronger rule-out than rule-in characteristics for hospital admission and 72-hour mortality.
METHODS: This single-center observational study prospectively sampled consecutive ED presentations during a prespecified continuous 72-hour window, with triage-time variables and secondary outcomes obtained from hospital records. Patients classified as MTS Category 5 (Blue; nonurgent) by the expert reference standard were excluded from the analytic cohort. GPT-5.5 Thinking was prompted in a standardized single-shot format using structured clinical vignettes derived from triage data. The primary endpoint was agreement between model-assigned MTS categories and an expert reference standard established by two independent blinded emergency medicine specialist rater teams, with disagreements resolved through senior adjudication. Exact agreement, within-1-level agreement, overtriage, undertriage, and dangerous undertriage were calculated. Secondary analyses evaluated prediction of hospital admission and 72-hour mortality using diagnostic performance metrics.
RESULTS: A total of 1,841 patients were included. Overall agreement between GPT-5.5 Thinking and the expert reference standard was substantial (κ = 0.721; 95% CI 0.691-0.740). Exact agreement rate was 62.0%, and within-1-level agreement rate was 99.0%. Overtriage occurred in 35.3% of cases, undertriage in 2.7%, and dangerous undertriage in 0.2%. Exact agreement rate was significantly lower in patients aged ≥ 65 years than in younger adults (57.1% vs. 64.2%, p = 0.013). For hospital admission, accuracy rate was 79.9%, sensitivity 83.8%, positive predictive value (PPV) 45.2%, and negative predictive value (NPV) 95.9%. For 72-hour mortality, accuracy rate was 86.5%, sensitivity 72.3%, PPV 12.6%, and NPV 99.1%.
CONCLUSION: Within this selected ED cohort, GPT-5.5 Thinking showed substantial agreement with expert MTS-based ED triage and generally preserved the ordinal hierarchy of clinical urgency. Its performance was characterized by frequent adjacent-level agreement, predominant overtriage, and very rare but nonzero dangerous undertriage. The model also showed stronger rule-out than rule-in characteristics for hospital admission and 72-hour mortality.