Predictive validity comparison of two five-level triage acuity scales
Bibliographic record
Abstract
INTRODUCTION: Each of the two most commonly used five-level triage tools in North America, the Emergency Severity Index and the Canadian Triage and Acuity Scale have been used as a measure of emergency department resource utilization in addition to acuity. In both cases, it is believed that patients triaged as having a higher level of acuity require a greater number of emergency department resources. We compared the ability of each tool to predict the emergency department resources for each emergency department visit and associated hospital admission and in-hospital mortality rates. METHODS: This is an observational, cohort study of a population-based random sample of patients triaged at two emergency departments over a 4-month period. Correlational analyses were performed to examine the relationship between the triage assessment and: (i) resource utilization, (ii) hospital admission, and (iii) in-hospital mortality. RESULTS: From 486 patients, analyses revealed the greatest correlation was between Emergency Severity Index and diagnostic resources [-0.54 (95% confidence intervals: -0.58, -0.50)] and the poorest correlation was between Canadian Triage and Acuity Scale and mortality [-0.16 (95% confidence intervals: -0.20, -0.12)]. No statistically significant differences (P<0.005) were observed between each tool 's ability to predict any of the outcomes measured. CONCLUSION: No statistically significant difference was observed in the ability of Emergency Severity Index v. 3 and Canadian Triage and Acuity Scale to predict emergency department resource utilization or immediate patient outcomes. This ability is, at best, only moderate indicating that other, more accurate tools than measures of triage acuity are required for this purpose.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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".