Urban-rural differences in pediatric ATV-related trauma in Canada from 2002-2019: A population-based descriptive study
Bibliographic record
Abstract
Abstract Objective The aim of this study was to describe differences and trends in ATV-related hospitalizations for urban and rural-dwelling youth in Canada. Methods We conducted a cross-sectional study using administrative hospital abstract data all patients admitted for an ATV-related injury to hospitals in 9 provinces in Canada between 2002 and 2019. The primary exposure was rural residence, defined by postal code. Rural-urban comparisons were stratified by age group: children (<16 years), adolescents (16-20 years) and adults (>21 years). The primary outcome was the incidence of any hospitalization, secondary outcomes were head injury, fractures, crush injury and spinal cord injury.. Results Among 34,390 patients with complete data, 17% were children younger than 16 yrs and 14% were adolescents 16-20 yrs; 78% of children and 85% of adolescents were male, and 47% lived rurally. The incident rate ratio (IRR) for being hospitalized for an ATV-related injury was 5-fold higher for rural children (5.59; 95% CI: 5.30-5.88) and adolescents (5.16; 95% CI: 4.88, 5.47) compared to urban children and adolescents, respectively. The 5-fold higher IRR was also evident for ATV-related fractures among rural children and adolescents. Adolescents had a particularly higher risk for ATV-related crush injuries (IRR: 10.43; 95% CI: 5.74-18.96) and spinal cord injuries (IRR: 5.21; 95% CI: 3.33-8.15) while children were at higher risk of ATV-related head injuries (IRR: 6.55; 95% CI: 5.76-7.46) compared to urban dwelling youth. Conclusions In Canada, rural children and adolescents were at a very elevated risk of ATV injuries compared to those living in urban centres.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".