Validation of the Taiwan triage and acuity scale: a new computerised five-level triage system
Bibliographic record
Abstract
OBJECTIVE: An ideal emergency department (ED) triage system accurately prioritises patients on the basis of the urgency of interventions required to avoid under- or over-triage. The objective of this study was to develop and validate a five-level Taiwan triage and acuity scale (TTAS) with an electronic decision support tool. METHODS: This prospective, multicentre, observational study included 10533 patients triaged at 11 academic medical centres, 18 regional and four district hospitals. Adult patients presenting to the ED were independently triaged by the duty triage nurse in the usual way and trained research nurses using TTAS with a computerised decision support system. Weighted κ statistics were used to assess the reproducibility. Hospitalisation, length of stay, and medical resource consumption were analysed by TTAS acuity levels. RESULTS: Most cases were stratified into levels 2 to 3 by the existing four-level triage system, whereas the TTAS stratified most patients to levels 3 (41.4%) and 4 (25.0%), and only a small number to level 1 (3.9%) (resuscitation; most urgent). Weighted κ for TTAS assignment was 0.87 (95% CI 0.85 to 0.89). The decrease in mean medical resource consumption and hospitalisation rate was statistically significant with each decrease in the TTAS triage acuity level. The length of stay also decreased significantly as the TTAS level acuity fell from levels 2 to 5. CONCLUSIONS: The TTAS was found to be a reliable triage system that accurately prioritises the treatment needed to avoid overtriage, more efficiently deploying the appropriate resources to ED patients.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".