Evaluation of activation criteria in paediatric multi-trauma
Bibliographic record
Abstract
Objective: To explore the optimal set of trauma activation criteria predicting paediatric patients' need for acute care following multi-trauma, with particular attention to Glasgow Coma Scale (GCS) cut-off value. Methods: A retrospective cohort study of paediatric multi-trauma patients aged 0 to 16 years, performed at a Level 1 paediatric trauma centre. Trauma activation criteria and GCS levels were examined with respect to patients' need for acute care, defined as: direct to operating room disposition, intensive care unit admission, need for acute interventions in the trauma room, or in-hospital death. Results: We enrolled 436 patients (median age 8.0 years). The following predicted need for acute care: GCS <14 (adjusted odds ratio [aOR] 23.0, 95% confidence interval [CI]: 11.5 to 45.9, P < 0.001), hemodynamic instability: (aOR 3.7, 95% CI: 1.2-8.1, P = 0.01), open pneumothorax/flail chest (aOR: 20.0, 95% CI: 4.0 to 98.7, P < 0.001), spinal cord injury (aOR 15.4, 95% CI; 2.4 to 97.1, P = 0.003), blood transfusion at the referring hospital (aOR: 7.7, 95% CI: 1.3 to 44.2, P = 0.02) and GSW to the chest, abdomen, neck, or proximal extremities (aOR 11.0, 95% CI; 1.7 to 70.8, P = 0.01). Using these activation criteria would have decreased over- triage by 10.7%, from 49.1% to 37.2% and under-triage by 1.3%, from 4.7% to 3.5%, in our cohort of patients. Conclusions: Using GCS<14, hemodynamic instability, open pneumothorax/flail chest, spinal cord injury, blood transfusion at the referring hospital, and GSW to the chest, abdomen, neck of proximal extremities, as T1 activation criteria could decrease over- and under-triage rates. Prospective studies are needed to validate the optimal set of activation criteria in paediatric patients.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".