Reliability of a Computerized Version of the Pediatric Canadian Triage and Acuity Scale
Bibliographic record
Abstract
BACKGROUND: The use of a standardized triage tool allows better comparison of the patients; a computerized version could theoretically improve its reliability. OBJECTIVES: To compare the interrater agreement of the Pediatric Canadian Triage and Acuity Scale (PedCTAS) and a computerized version (Staturg). METHODS: A two-phase experimental study was conducted to compare the interrater agreement between nurses assigning triage level to written case scenarios using either traditional PedCTAS or Staturg. Participants were nurses with at least one year of experience in pediatric emergency medicine and trained at triage. Each of the 54 scenarios was evaluated first by all nurses using either one of the strategies. Four weeks later, they evaluated the same scenarios using the other tool. The primary outcome was the interrater agreement measured using kappa score. RESULTS: Eighteen of the 29 eligible nurses participated in the study. The computerized triage tool showed a better interrater agreement, with a Staturg kappa score of 0.55 (95% confidence interval = 0.53 to 0.57) versus a PedCTAS kappa score of 0.51 (95% confidence interval = 0.49 to 0.53). The computerized version was also associated with higher agreements for scenarios describing patients with the highest severity of triage (kappa score of 0.72 vs. 0.55 for level 1; kappa score of 0.70 vs. 0.51 for level 2). CONCLUSIONS: A computerized version of the PedCTAS showed a statistically significant improvement in the interrater agreement for nurses evaluating the triage level of 54 clinical scenarios, but this difference has probably small clinical significance.
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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 | 0.012 | 0.063 |
| 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 | 0.000 | 0.000 |
| 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".