Emergency Triage: Comparing a Novel Computer Triage Program with Standard Triage
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".