Injuries and helmet use related to non-motorized wheeled activities among pediatric patients
Bibliographic record
Abstract
INTRODUCTION: Patients presenting to emergency departments (ED) for injuries resulting from recreational activities represent a unique source of information on important directions for injury prevention efforts. We describe the epidemiology of non-motorized wheeled activity-related injury in pediatric patients presenting to Canadian EDs as well as patients' helmet use. METHODS: Data for the years 2004 to 2009 were abstracted from the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP), a national ED injury surveillance program in fifteen hospitals. RESULTS: Most of the 28 618 children aged 1 to 16 years injured during non-motorized wheeled activities were injured while cycling, followed by skateboarding. Most injuries occurred among boys. Children injured on scooters tended to be younger whereas skateboarders were the oldest. On average, the number of all injuries decreased by 6% over the time period. Falls were the most common mechanism of injury; 8.3% of patients had head injuries, which were seen more often among cyclists than other wheeled-activity users. Helmet use was greatest among cyclists (62.2%) and lowest among skateboarders (32.9%). Injured patients presenting to EDs in jurisdictions with legislation mandating helmet use had 2.12 greater odds of helmet use and 0.86 lesser odds of head injury compared with those presenting in jurisdictions without helmet laws. CONCLUSION: These results provide further evidence that legislation mandating helmet use may be an effective way of reducing injury among all wheeled-activity users. The small number of patients who presented with helmet use and protective gear (59.4% overall) suggests that this remains an area for intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".