Pediatric emergency triage systems
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to perform a narrative review of the leading pediatric triage systems in emergency departments (EDs). DATA SOURCE: Articles published between 1999 and 2019 were identified by searching the MEDLINE, EMBASE, and PubMed databases using the keywords "pediatric triage", "pediatric assessment tools", and "emergency department triage" with an emphasis on studies that evaluated the validation and reliability of triage systems. DATA SYNTHESIS: A total of 105 articles on pediatric emergency triage systems in 12 countries were evaluated. Triage systems were divided into two groups: color-stratified triage systems and alert systems. The color-stratified triage systems included in this review were the Canadian Triage and Acuity Scale (CTAS), Manchester Triage System (MTS), Emergency Severity Index (ESI), and Australasian Triage Scale (ATS), and the alert systems included were the Paediatric Observation Priority Score (POPS), Pediatric Early Warning Score (PEWS), and Pediatric Approach Triangle (PAT). Evidence corroborates the validity and reliability of MTS, PaedCTAS, ESI version 4, PEWS, POPS, and PAT in pediatric emergency services. CONCLUSIONS: These are fundamental tools for risk classification of patients seeking treatment in EDs. Not all triage systems have been assessed for validity and reliability; nor are they well suited for all regions of the world. Employing triage systems in Brazil requires cultural adaptation and rigorous training of the local health staff, in addition to validation and reliability studies in our country, since the social and cultural context of this country differs from those where these tools were developed.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".