"Beyond Vital Signs: Advancing Triage Systems Specific to Pediatric Emergencies for Safer and Smarter Acute Care"
Bibliographic record
Abstract
Abstract: Triage in pediatric emergency settings is a critical process that determines the prioritization of care for infants, children, and adolescents presenting with a wide spectrum of clinical conditions. Unlike adult triage, pediatric triage must account for developmental variations, age-specific physiological norms, communication barriers, dependency status, and distinct disease patterns. Errors in triage may result in delayed treatment, adverse outcomes, and increased mortality. Over the past two decades, several structured triage systems have been developed and validated internationally, including the Emergency Severity Index (ESI), Manchester Triage System (MTS), Canadian Triage and Acuity Scale (CTAS), and the Australasian Triage Scale (ATS). Many of these include pediatric adaptations or dedicated pediatric modifiers. This review article critically examines pediatric-specific triage systems, their theoretical underpinnings, reliability, validity, implementation challenges, and implications for nursing practice. The article also explores innovations such as pediatric early warning scores and digital triage tools. Strengthening pediatric triage processes is essential for enhancing patient safety, optimizing resource allocation, and improving clinical outcomes in emergency departments worldwide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".