External Validation and Modification of a Pediatric Trauma Triage Tool
Bibliographic record
Abstract
BACKGROUND: Simon et al. developed a simple secondary triage tool (mPTS) based on physiologic parameters and physical findings to identify pediatric trauma patients who had a low likelihood of serious injury. Such patients could be treated in the emergency room without full trauma team activation. Our objective was to evaluate the mPTS on the trauma population at our institution, a Level I pediatric trauma center. METHODS: This was a retrospective cohort study of all trauma team activations at The Hospital for Sick Children (Sick Kids) (1999-2002), excluding penetrating trauma and burns. Patients were stratified into high-risk (Injury Severity Score [ISS] >or=12) and low-risk (ISS <12) groups. The mPTS evaluates airway integrity, open wounds, neurologic status, hemodynamics, and skeletal integrity and applies a score of 1 point to each criterion. RESULTS: There were 628 trauma patients (382 boys, mean age of 8 +/- 3.8 years). The mPTS had a sensitivity of 92% and a positive predictive value (PPV) of 47% when applied to our population. The mPTS missed 21 patients with significant injuries, many were intra-abdominal. We modified the mPTS to include contusions to head and/or torso and a history of loss of consciousness and a 7-point score was developed. After modification the sensitivity was 99%, specificity 21%, and PPV of 46% with a 20% reduction in unnecessary trauma team activations. CONCLUSIONS: The original mPTS by Simon et al. was not sensitive enough when applied to our population. The Sick Kids modification to the score improved the sensitivity to 99%. The PPV of 46% indicates a safe level of overtriage is maintained. The Sick Kids mPTS remains easy to apply and would have reduced trauma team activation by 20%.
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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 | 0.043 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".