A Scoping Review of Pediatric Mass-Casualty Incident Triage Algorithms
Bibliographic record
Abstract
OBJECTIVE: For the pediatric population, there is no consensus on which triage system to use for mass-casualty incidents (MCI). A scoping review was conducted to identify the most accurate triage system for pediatric patients in MCIs. METHODS: MEDLINE (NLM, Bethesda, MA, USA), Embase (Elsevier Inc., Amsterdam, Netherlands), CINAHL (EBSCO Information Services, Ipswitch, MA, USA), and The Cochrane CENTRAL Register of Controlled Trials (John Wiley & Sons, Hoboken, NJ, USA), as well as Scopus (Elsevier Inc., Amsterdam, Netherlands), Global Health (Centre for Agriculture and Bioscience International, Wallingford, UK), Global Health Archive (Centre for Agriculture and Bioscience International, Wallingford, UK), and Global Index Medicus (World Health Organization, Geneva, Switzerland) were searched for relevant studies that were divided into 3 categories: accuracy of a single system, comparison of 2 or more primary triage system and comparison of secondary triage systems. Grey literature was also searched. RESULTS: 996 studies were identified from which 18 studies were included. Systems studied were found to have poor inter-rater reliability, had a low level of agreement between providers, had missed critically ill patients or were not externally validated. 11 studies compared pediatric MCI triage algorithms using different strategies and the most accurate algorithm was not identified. A recently developed secondary triage system, specifically for pediatric patients, was found to perform better than the comparison triage system. CONCLUSION: Although some algorithms performed better than others, no primary triage algorithm was accurate enough for the pediatric population. However, only 1 secondary triage algorithm was found to be superior to the others.
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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 | 0.029 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.026 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".