Off-Road Vehicle Crash Risk during the Six Months after a Birthday
Bibliographic record
Abstract
BACKGROUND: Off-road vehicles are popular and thrilling for youth outside urban settings, yet sometimes result in a serious crash that requires emergency medical care. The relation between birthdays and the subsequent risk of an off-road vehicle crash is unknown. METHODS: We conducted a population-based before-and-after longitudinal analysis of youth who received emergency medical care in Ontario, Canada, due to an off-road vehicle crash between April 1, 2002, and March 31, 2014. We identified youth injured in an off-road vehicle crash through population-based health-care databases of individuals treated for medical emergencies. We included youth aged 19 years or younger, distinguishing juniors (age ≤ 15 years) from juveniles (age ≥ 16 years). RESULTS: A total 32,777 youths accounted for 35,202 emergencies due to off-road vehicle crashes within six months of their nearest birthday. Comparing the six months following a birthday to the six months prior to a birthday, crashes increased by about 2.7 events per 1000 juniors (18.3 vs 21.0, p < 0.0001). The difference equaled a 15% increase in relative risk (95% confidence interval 12 to 18). The increase extended for months following a birthday, was not observed for traffic crashes due to on-road vehicles, and was partially explained by a lack of helmet wearing. As expected, off-road crash risks did not change significantly following a birthday among juveniles (19.2 vs 19.8, p = 0.61). CONCLUSIONS: Off-road vehicle crashes leading to emergency medical care increase following a birthday in youth below age 16 years. An awareness of this association might inform public health messages, gift-giving practices, age-related parental permissions, and prevention by primary care physicians.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".