162: The Big Score & Prediction of Mortality in Pediatric Trauma
Bibliographic record
Abstract
Trauma is the leading cause of death in children. The limitations of previous trauma mortality prediction scores include their complexity and lack of validation. In contrast, the BIG score is simple, easy to calculate, and incorporates key physiologic variables influencing trauma mortality: base-deficit, INR and Glascow Coma Scale- with encouraging validation results in Germany. However, the median age was higher than at most North American centers and other factors impacting mortality were not examined. We examined the association between in-hospital mortality and the BIG score measured on Emergency Department arrival in pediatric blunt trauma patients, adjusted for age, pre-hospital intubation, volume administration and referral hospital management. We also examined the association between the BIG score and mortality in patients requiring ICU care. A retrospective 2001–2012 trauma registry review of blunt trauma patients ≤17 years old. Charts were reviewed for in-hospital mortality, age, components of the BIG score on Emergency Department arrival, pre-hospital intubation, administration of a crystalloid bolus ≥20 mL/kg, local hospital referral and disposition. We found that 50/621 (8%) study patients died. Independent mortality predictors were the BIG score (OR 12, 95% CI 6–25), prior fluid bolus (OR 3, 95% CI 1.3–9) and prior intubation (OR 8, 95% CI 2–40). The area under the ROC curve was 0.95 (CI 0.93–0.98), with the optimal BIG cutoff of 16. With BIG <10, death rate was 1/382 (0.003, 95% CI 0.001–0.007), versus 23/209 (0.11, 95% CI 0.7–0.15) with BIG 11–25, and 26/30 (0.87, 95% CI 0.67–0.95) with BIG >25 (P<0.0001). In patients requiring the ICU, the BIG score remained predictive of mortality (OR 14.3, 95% CI 7.3–32, P<0.0001). The BIG score accurately predicts mortality in North American pediatric blunt trauma patients independent of age or pre-hospital interventions, identifies children with a high probability of survival (BIG ≤10) and those with highest potential of benefit from timely interventions (BIG 11–25). The BIG score is also associated with mortality in pediatric trauma patients requiring ICU care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".