Increased prehospital mortality in patients with combined burns and trauma in Canada: Analysis of a provincial trauma registry database
Bibliographic record
Abstract
INTRODUCTION: The combination of burns and non-thermal trauma may have a synergistic effect on mortality. Our objective was to determine if burn patients with concomitant trauma are at increased risk of mortality in both the prehospital and in-hospital settings. METHODS: Data were collected from a population-based provincial trauma registry (2001-2019). Characteristics and outcomes of patients with trauma/burns were compared to isolated burn patients using t-tests, chi-square analysis and Fisher's exact tests. Risk ratios (RRs) were calculated to evaluate the impact of concomitant trauma on mortality, stratified by % total body surface area (TBSA) and injury severity score (ISS). Firth's penalized maximum likelihood estimation (PMLE) approach was used to fit multivariable logistic regression models to the outcomes of prehospital mortality and in-hospital mortality. RESULTS: Of 436 burn patients, 29.8 % (130/436) had combined trauma/burns. Prehospital mortality in the trauma/burns group was 57.7 % (75/130) versus 43.1 % (132/306) in isolated burn patients. Prehospital mortality risk was highest in trauma/burn patients with % TBSA ≥ 70 (RR 3.87, 95 % CI 2.99-4.99) or ISS ≥ 25 (RR 2.49, 95 % CI 1.84-3.36). Concomitant trauma was associated with increased odds of prehospital mortality (OR 2.42, 95 % CI 1.27-4.69), but had no impact on in-hospital mortality. CONCLUSIONS: Prehospital mortality was increased in patients with combined burns and trauma.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".