Predictive Validity of a Computerized Emergency Triage Tool
Bibliographic record
Abstract
BACKGROUND: Emergency department (ED) triage prioritizes patients on the basis of the urgency of need for care. eTRIAGE is a Web-based triage decision support tool that is based on the Canadian Triage and Acuity Scale (CTAS), a five level triage system (CTAS 1 = resuscitation, CTAS 5 = nonurgent). OBJECTIVES: To examine the validity of eTRIAGE on the basis of resource utilization and cost as measures of acuity. METHODS: Scores on the CTAS, specialist consultations, computed-tomography use, ED length of stay, ED disposition, and estimated ED and hospital costs (if the patient was subsequently admitted to hospital) were collected for each patient over a six month period. These data were queried from a database that captures all regional ED visits. Correlations between CTAS score and each outcome were measured by using logistic regression models (categorical variables), univariate analysis of variance (continuous variables), and the Kruskal-Wallis analysis of variance (costs). A multivariate regression model that used cost as the outcome was used to identify interaction between the variables presented. RESULTS: Over the six month study, 29,524 patients were triaged by using eTRIAGE. When compared with CTAS level 3, the odds ratios for consultation, CT scan, and admission were significantly higher in CTAS 1 and 2 and were significantly lower in CTAS 4 and 5 (p < 0.001). When compared with CTAS levels 2-5 combined, the odds ratio for death in CTAS 1 was 664.18 (p < 0.001). The length of stay also demonstrated significant correlation with CTAS score (p < 0.001). Costs to the ED and hospital also correlated significantly with increasing acuity (median costs for CTAS levels in Canadian dollars: CTAS 1 = 2,690 dollars, CTAS 2 = 433 dollars, CTAS 3 = 288 dollars, CTAS 4 = 164 dollars, CTAS 5 = 139 dollars, and p < 0.001). Significant interactions between the data collected were found in a multivariate regression model, although CTAS score remained highly associated with costs. CONCLUSIONS: Acuity measured by eTRIAGE demonstrates excellent predictive validity for resource utilization and ED and hospital costs. Future research should focus on specific presenting complaints and targeted resources to more accurately assess eTRIAGE validity.
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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 | 0.006 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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".