Profile of trauma mortality and trauma care resources at rural emergency departments and urban trauma centres in Quebec: a population-based, retrospective cohort study
Bibliographic record
Abstract
OBJECTIVES: As Canada's second largest province, the geography of Quebec poses unique challenges for trauma management. Our primary objective was to compare mortality rates between trauma patients treated at rural emergency departments (EDs) and urban trauma centres in Quebec. As a secondary objective, we compared the availability of trauma care resources and services between these two settings. DESIGN: Retrospective cohort study. SETTING: 26 rural EDs and 33 level 1 and 2 urban trauma centres in Quebec, Canada. PARTICIPANTS: 79 957 trauma cases collected from Quebec's trauma registry. PRIMARY AND SECONDARY OUTCOME MEASURES: Our primary outcome measure was mortality (prehospital, ED, in-hospital). Secondary outcome measures were the availability of trauma-related services and staff specialties at rural and urban facilities. Multivariable generalised linear mixed models were used to determine the relationship between the primary facility and mortality. RESULTS: Overall, 7215 (9.0%) trauma patients were treated in a rural ED and 72 742 (91.0%) received treatment at an urban centre. Mortality rates were higher in rural EDs compared with urban trauma centres (13.3% vs 7.9%, p<0.001). After controlling for available potential confounders, the odds of prehospital or ED mortality were over three times greater for patients treated in a rural ED (OR 3.44, 95% CI 1.88 to 6.28). Trauma care setting (rural vs urban) was not associated with in-hospital mortality. Nearly all of the specialised services evaluated were more present at urban trauma centres. CONCLUSIONS: Trauma patients treated in rural EDs had a higher mortality rate and were more likely to die prehospital or in the ED compared with patients treated at an urban trauma centre. Our results were limited by a lack of accurate prehospital times in the trauma registry.
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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 | 0.000 |
| 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) | 0.001 | 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 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".