All-terrain vehicle serious injuries and death in children and youth: A national survey of Canadian paediatricians
Bibliographic record
Abstract
BACKGROUND: All-Terrain Vehicles (ATVs) are a leading cause of serious injury in children and youth. Certain Canadian regions have implemented legislation to promote safety, including age restrictions, mandatory training and helmet use. Jurisdictions with more stringent ATV safety legislation have been shown to have reduced injury rates in the short term. OBJECTIVES: To estimate the burden of ATV-related serious injury and death in Canada and to identify Canadian physicians' knowledge of ATV-related legislation, safety and health promotion practices. METHODS: A one-time survey was distributed to practicing paediatricians and paediatric subspecialists participating in the Canadian Paediatric Surveillance Program (CPSP) in October 2016. RESULTS: Of 2793 physicians contacted, 904 responded (32.4%). There were 181 reported cases of serious and/or fatal ATV-related injuries, including 6 deaths. Children aged 10 to 14 represented the most number of cases (n=82, 45.3%), followed by 15 to 19 (n=48, 26.5%) and 5 to 9 (n=40, 22.1%). Most cases occurred in July/August (48.3%) and May/June (25.2%), were in males (n=133, 78.2%), and occurred during recreational activity (n=139, 83.2%) or organized racing (n=6, 3.6%). In 99 cases (58.9%), the child was the driver of the ATV. Only two-thirds of respondents (67.5%) knew that ATVs should not carry passengers while under half (42.2%) never discussed ATV safety with their patients. CONCLUSIONS: ATV-related injuries and deaths in Canadian children remain a serious public health problem. Education of health care practitioners, including paediatricians, is needed to promote safety. Despite efforts to reduce ATV-related injuries, there remains a significant number of serious injuries and/deaths related to their use.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".