Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-1421
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-15T06:55:08.347142Z
status: published
lens: trace
sector: health
headline: Agreement and classification performance of ChatGPT-5-generated and nurse-led emergency department triage using an expert reference decision: A retrospective study
dek: This study evaluated the agreement and classification performance of ChatGPT-5-generated triage compared with nurse triage using an expert reference decision in adult emergency department patients. In this retrospective cross-sectional study, 1564 adult patients with documented yellow or green triage classifications were included. Standardized triage-level patient information was entered into ChatGPT-5 using an identical prompt for all patients. Agreement was assessed using Cohen's κ, and classification performa…
gain_title: For adult emergency patients classed yellow or green, ChatGPT-5 triage reached higher accuracy and sensitivity against an expert reference than nurse triage, so it undertriaged fewer yellow patients.
problem_title: In expert-reference green patients, ChatGPT-5 triage assigned yellow more often than nurses did (15.0% vs. 2.3%), which suggests more over-triage of low-acuity patients.
trace_subject: ChatGPT-5 versus nurse triage of yellow and green adult emergency department patients
gain_reading: For adult emergency patients classed yellow or green, ChatGPT-5 triage reached higher accuracy and sensitivity against an expert reference than nurse triage, so it undertriaged fewer yellow patients.
gain_evidence: higher overall accuracy (83.5% vs. 73.3%), and higher sensitivity (82.0% vs. 49.0%) | produced fewer green classifications among expert-reference yellow patients (18.0% vs. 51.0%)
problem_reading: In expert-reference green patients, ChatGPT-5 triage assigned yellow more often than nurses did (15.0% vs. 2.3%), which suggests more over-triage of low-acuity patients.
problem_evidence: whereas nurse triage produced fewer yellow classifications among expert-reference green patients (2.3% vs. 15.0%)
quick_read: Researchers compared ChatGPT-5 triage and nurse triage with an expert reference decision in a retrospective review of 1564 adult emergency department patients documented as yellow or green. The same prompt was used for every patient, and standardized triage-level information was entered into the model.

ChatGPT-5 agreed more closely with the experts (κ 0.670 vs. 0.467) and was more accurate and more sensitive. The result suggests AI could support triage in ambiguous lower-acuity cases. It was retrospective and used only yellow and green categories, so it does not show how the tool would perform live or for more urgent patients. The cost of its extra yellow assignments for green patients is also unclear.
limitation: The findings apply only to the conditions evaluated: retrospective data on adult patients with documented yellow or green classifications, so performance on other acuity levels or in live use is not established.
tag: Dual reading
key_points: The study included 1564 adult patients with documented yellow or green triage classifications. | Agreement with the expert reference was κ = 0.670 for ChatGPT-5 and 0.467 for nurses. | Overall accuracy was 83.5% for ChatGPT-5 and 73.3% for nurse triage. | Sensitivity for yellow classification was 82.0% for ChatGPT-5 versus 49.0% for nurses. | ChatGPT-5 over-assigned yellow to expert-green patients more often than nurses did (15.0% vs. 2.3%).
rundown: The design was retrospective and cross-sectional. Standardized triage-level patient information was entered into ChatGPT-5 with an identical prompt, and agreement was measured with Cohen's κ alongside sensitivity, specificity and accuracy.

The cohort had a mean age of 39.8 years and was 55.9% female. Experts classified half of the patients as yellow. ChatGPT-5 classified 48.5% as yellow, whereas nurses classified only 25.6% as yellow.

Nurses sent 51.0% of expert-reference yellow patients to green, compared with 18.0% for ChatGPT-5. The trade-off ran the other way for green patients, where ChatGPT-5 gave yellow more often. The authors conclude that ChatGPT-5 approximated the expert decision more closely only under the conditions tested.
sources:
- peer_reviewed | International Emergency Nursing | https://doi.org/10.1016/j.ienj.2026.101918 | 2026-10-12
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

48c5ade12d3053eba3b0fed809019f152498691d91eec5f15509bd1d5dd52ecf

previous

0000000000000000000000000000000000000000000000000000000000000000