Access to pediatric trauma centres in Canada: a population-based retrospective cohort study
Bibliographic record
Abstract
Background: Children with major trauma have better outcomes when treated in pediatric trauma centres, but population-based data on access to these centres in Canada are lacking. We aimed to estimate the proportion of children with major trauma who accessed a pediatric trauma centre in Canada (through direct transport or transfer) and compare access across provinces. Methods: We conducted a population-based cohort study of children (aged < 16 yr) who were admitted to hospital after a major trauma (Injury Severity Score > 12) in 9 Canadian provinces (excluding Quebec) from 2016 to 2021. We estimated the adjusted incidence of access to a pediatric trauma centre across provinces using robust Poisson regression and examined the effect of age and injury severity in subgroup analyses. Results: Of 3007 children with major trauma, 2335 (77.6%) were directly transported (n = 879, 29.2%) or transferred (n = 1456, 48.4%) to a pediatric trauma centre. Crude access to pediatric trauma centres was higher for younger children (80.9% among those aged 0 to 5 yr, 81.7% among those aged 6 to 12 yr, 69.9% among those aged 13 to 15 yr) and those with critical injuries (88.8%). Adjusted pediatric trauma centre access was lower in British Columbia (relative risk [RR] 0.68, 95% confidence interval [CI] 0.63 to 0.74), the Atlantic provinces (RR 0.80, 95% CI 0.73 to 0.88), and Saskatchewan (RR 0.77, 95% CI 0.69 to 0.86) than Ontario, but was higher in Alberta (RR 1.06, 95% CI 1.02 to 1.10) and Manitoba (RR 1.14, 95% CI 1.09 to 1.19). Interprovincial differences were present across all subgroups (p < 0.0001). Interpretation: Across 9 Canadian provinces, 1 in 4 children with major trauma did not receive care in a pediatric trauma centre. These results suggest the opportunity for improvement in Canadian trauma systems to ensure that all children receive optimal injury care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".