The Validity of the Pediatric Assessment Triangle as the First Step in the Triage Process in a Pediatric Emergency Department
Bibliographic record
Abstract
OBJECTIVE: This study aimed to assess the association between pediatric assessment triangle (PAT) findings during triage and markers of severity in a pediatric emergency department (PED). METHODS: During the study period, patients arriving to the PED were classified by trained nurses with the Pediatric Canadian Triage and Acuity Scale using a computer system, from which data were obtained and analyzed retrospectively. The primary outcome measure was the percentage of children hospitalized related with PAT findings. The secondary outcome measures were the admission to the intensive care unit (%), PED length of stay, and performance of blood tests (%). RESULTS: Among the 302,103 episodes included, there were abnormal PAT findings in 24,120 cases (7.9%). Multivariate analysis adjusted for age confirmed that PAT findings and triage level were independent risk factors for admission (odds ratio [OR], 2.21; 95% confidence interval [CI], 2.13-2.29; OR, 6.01; 95% CI, 5.79-6.24, respectively). Abnormal findings in appearance or in more than 1 PAT component were even more strongly associated with admission (3.99; 95% CI, 3.63-4.38; 14.99, 95% CI, 11.99-18.74, respectively). When adjusted for triage level and age, abnormal PAT findings were also an independent risk factor for intensive care unit admission (OR, 4.44; 95% CI, 3.77-5.24) and a longer stay in the PED (OR, 1.78; 95% CI, 1.72-1.84). CONCLUSIONS: Abnormal findings in the PAT applied by trained nurses at triage identify patients with a higher risk of hospitalization. The PAT seems to be a valid tool for identifying the most severe patients as a first step in the triage process.
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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 | 0.007 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".