Emergency Triage: Comparing a Novel Computer Triage Program with Standard Triage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Emergency department (ED) triage prioritizes patients based on urgency of care; however, little previous testing of triage tools in a live ED environment has been performed. OBJECTIVES: To determine the agreement between a computer decision tool and memory-based triage. METHODS: Consecutive patients presenting to a large, urban, tertiary care ED were assessed in the usual fashion and by a blinded study nurse using a computerized decision support tool. Triage score distribution and agreement between the two triage methods were reported. A random subset of patients was selected and reviewed by a blinded expert panel as a consensus standard. RESULTS: Over five weeks, 722 ED patients were assessed; complete data were available from 693 (96%) score pairs. Agreement between the two methods was poor (kappa = 0.202; 95% confidence interval [95% CI] = 0.150 to 0.254); however, agreement improved when using weighted kappa (0.360; 95% CI = 0.305 to 0.415) or "within one" level kappa (0.732; 95% CI = 0.644 to 0.821). When compared with the expert panel, the nurse triage scores showed lower agreement (0.263; 95% CI = 0.133 to 0.394) than the tool (kappa = 0.426; 95% CI = 0.289 to 0.564). There was a significant down-triaging of patients when patients were triaged without the computerized tool. Admission rates also differed between the triage systems. CONCLUSIONS: There was significant discrepancy by nurses using memory-based triage when compared with a computer tool. Triage decision support tools can mitigate this drift, which has administrative implications for EDs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it