Performance of triage systems in emergency care: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To assess and compare the performance of triage systems for identifying high and low-urgency patients in the emergency department (ED). DESIGN: Systematic review and meta-analysis. DATA SOURCES: EMBASE, Medline OvidSP, Cochrane central, Web of science and CINAHL databases from 1980 to 2016 with the final update in December 2018. ELIGIBILITY CRITERIA: Studies that evaluated an emergency medical triage system, assessed validity using any reference standard as proxy for true patient urgency and were written in English. Studies conducted in low(er) income countries, based on case scenarios or involving less than 100 patients were excluded. REVIEW METHODS: Reviewers identified studies, extracted data and assessed the quality of the evidence independently and in duplicate. The Quality Assessment of studies of Diagnostic Accuracy included in Systematic Reviews -2 checklist was used to assess risk of bias. Raw data were extracted to create 2×2 tables and calculate sensitivity and specificity. ED patient volume and casemix severity of illness were investigated as determinants of triage systems' performance. RESULTS: Sixty-six eligible studies evaluated 33 different triage systems. Comparisons were restricted to the three triage systems that had at least multiple evaluations using the same reference standard (Canadian Triage and Acuity Scale, Emergency Severity Index and Manchester Triage System). Overall, validity of each triage system to identify high and low-urgency patients was moderate to good, but performance was highly variable. In a subgroup analysis, no clear association was found between ED patient volume or casemix severity of illness and triage systems' performance. CONCLUSIONS: Established triage systems show a reasonable validity for the triage of patients at the ED, but performance varies considerably. Important research questions that remain are what determinants influence triage systems' performance and how the performance of existing triage systems can be improved.
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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 | 0.031 | 0.085 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.050 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".