Comparison of trauma care structures, processes and outcomes between the English National Health Service and Quebec, Canada
Bibliographic record
Abstract
Background: Comparisons across trauma systems are key to identifying opportunities to improve trauma care. We aimed to compare trauma service structures, processes and outcomes between the English National Health Service (NHS) and the province of Quebec, Canada. Methods: We conducted a multicentre cohort study including admissions of patients aged older than 15 years with major trauma to major trauma centres (MTCs) from 2014/15 to 2016/17. We compared structures descriptively, and time to MTC and time in the emergency department (ED) using Wilcoxon tests. We compared mortality, and hospital and intensive care unit (ICU) length of stay (LOS) using multilevel logistic regression with propensity score adjustment, stratified by body region of the worst injury. Results: The sample comprised 36 337 patients from the NHS and 6484 patients from Quebec. Structural differences in the NHS included advanced prehospital medical teams (v. “scoop and run” in Quebec), helicopter transport (v. fixed-wing aircraft) and trauma team leaders. The median time to an MTC was shorter in Quebec than in the NHS for direct transports (1 h v. 1.5 h, p < 0.001) but longer for transfers (2.5 h v. 6 h, p < 0.001). Time in the ED was longer in Quebec than in the NHS (6.5 h v. 4.0 h, p < 0.001). The adjusted odds of death were higher in Quebec for head injury (odds ratio [OR] 1.28, 95% confidence interval [CI] 1.09–1.51) but lower for thoracoabdominal injuries (OR 0.69, 95% CI 0.52–0.90). The adjusted median hospital LOS was longer for spine, torso and extremity injuries in the NHS than in Quebec, and the median ICU LOS was longer for spine injuries. Conclusion: We observed significant differences in the structure of trauma care, delays in access and risk-adjusted outcomes between Quebec and the NHS. Future research should assess associations between structures, processes and outcomes to identify opportunities for quality improvement.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".