Validation of different pediatric triage systems in the emergency department
Bibliographic record
Abstract
BACKGROUND: Triage system in children seems to be more challenging compared to adults because of their different response to physiological and psychosocial stressors.This study aimed to determine the best triage system in the pediatric emergency department. METHODS:This was a prospective observational study.This study was divided into two phases.The fi rst phase determined the inter-rater reliability of fi ve triage systems: Manchester Triage System (MTS), Emergency Severity Index (ESI) version 4, Pediatric Canadian Triage and Acuity Scale (CTAS), Australasian Triage Scale (ATS), and Ramathibodi Triage System (RTS) by triage nurses and pediatric residents.In the second phase, to analyze the validity of each triage system, patients were categorized as two groups, i.e., high acuity patients (triage level 1, 2) and low acuity patients (triage level 3, 4, and 5).Then we compared the triage acuity with actual admission. RESULTS:In phase I, RTS illustrated almost perfect inter-rater reliability with kappa of 1.0 (P<0.01).ESI and CTAS illustrated good inter-rater reliability with kappa of 0.8-0.9(P<0.01).Meanwhile, ATS and MTS illustrated moderate to good inter-rater reliability with kappa of 0.5-0.7 (P<0.01).In phase II, we included 1 041 participants with average age of 4.7±4.2years, of which 55% were male and 45% were female.In addition 32% of the participants had underlying diseases, and 123 (11.8%) patients were admitted.We found that ESI illustrated the most appropriate predicting ability for admission with sensitivity of 52%, specifi city of 81%, and AUC 0.78 (95%CI 0.74-0.81).CONCLUSION: RTS illustrated almost perfect inter-rater reliability.Meanwhile, ESI and CTAS illustrated good inter-rater reliability.Finally, ESI illustrated the appropriate validity for triage system.
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".