Mortality in Canadian Trauma Systems
Bibliographic record
Abstract
OBJECTIVE: To measure the variation in trauma center mortality across Canadian trauma systems, assess the contribution of traumatic brain injury and thoracoabdominal injury to observed variations, and evaluate whether the presence of recommended trauma system components is associated with mortality. SUMMARY BACKGROUND DATA: Injuries represent one of the leading causes of mortality, disability, and health care costs worldwide. Trauma systems have improved injury outcomes, but the impact of trauma system configuration on mortality is unknown. METHODS: We conducted a retrospective cohort study of adults admitted for major injury to trauma centers across Canada (2006-2012). Multilevel logistic regression was used to estimate risk-adjusted hospital mortality and assess the impact of 13 recommended trauma system components. RESULTS: Of 78,807 patients, 8382 (10.6%) died in hospital including 6516 (78%) after severe traumatic brain injury and 749 (9%) after severe thoracoabdominal injury. Risk-adjusted mortality varied from 7.0% to 14.2% across provinces (P < 0.0001); 11.1% to 26.0% for severe traumatic brain injury (P < 0.0001), and 4.7% to 5.9% for thoracoabdominal injury (P = 0.2). Mortality decreased with increasing number of recommended trauma system elements; adjusted odds ratio = 0.93 (0.87-0.99). CONCLUSIONS: We observed significant variation in trauma center mortality across Canadian provinces, specifically for severe traumatic brain injury. Provinces with more recommended trauma system components had better patient survival. Results suggest that trauma system configuration may be an important determinant of injury mortality. A better understanding of which system processes drive optimal outcomes is required to reduce the burden of injury worldwide.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".