Injury-related deaths in the Ontario provincial trauma system: a retrospective population-based cohort analysis
Bibliographic record
Abstract
BACKGROUND: Although Ontario has an established trauma system, it experiences a substantial burden of morbidity and mortality from injury. Our objective was to describe patterns of fatal injury in Ontario, with a focus on location of death (out of hospital, trauma or non-trauma centre) and receipt of surgical intervention before death. METHODS: We conducted a retrospective population-based cohort study using linked administrative data on fatal injuries in children and adults (no age restrictions) in Ontario between 2000 and 2016. We identified injury-related deaths in the Ontario Registrar General Death database. We developed descriptive statistics for injury characteristics and causes of death. We calculated the fatal injury incidence rate for each year of the study. The primary outcome was cause of death; the secondary outcome was receipt of surgical intervention. RESULTS: The analysis included 19 408 people. The mean annual incidence of fatal injury averaged 8.7 (95% confidence interval 7.7-9.6) per 100 000. The most common mechanisms of injury were motor vehicle collisions (12 065, 62.2%), followed by gunshot wounds (3134, 16.1%) and falls (2387, 12.3%). Deaths frequently occurred out of hospital (72.6%), rather than at a trauma centre (14.2%) or non-trauma centre (13.2%). Patients treated at trauma centres were significantly more likely to receive a surgical intervention (standardized difference 0.6) than those treated at non-trauma centres. INTERPRETATION: Most injury deaths in Ontario occur in the out-of-hospital setting or are managed at non-trauma centres; many patients receive no surgical intervention before death. There are likely opportunities to improve access to specialized injury care in Ontario's trauma system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".