Epidemiology of sports-related injuries in children and youth presenting to Canadian emergency departments from 2007–2010
Bibliographic record
Abstract
BACKGROUND: Although injuries related to sports and recreation represent a significant burden to children and youth, few studies have examined the descriptive epidemiology of sports-related injury since 2005, and some sports such as ringette have not been evaluated to date. The primary purpose of this study was to provide the descriptive epidemiology of sports-related injuries treated in emergency departments for children and youth aged 5 - 19. METHODS: A retrospective data analysis was performed using data from the Canadian Hospitals Injury Reporting and Prevention Program [CHIRPP] from fiscal years (April - March) 2007/08 to 2009/10. CHIRPP is a computerized information system designed by the Public Health Agency of Canada that collects information about injuries to people evaluated in emergency departments across 11 pediatric hospitals and 5 general hospitals in Canada. Thirteen sports or activities were analyzed (baseball, basketball, cycling, football, ice hockey, lacrosse, ringette, rugby, skiing, sledding, snowboarding, soccer, and volleyball). Descriptive statistics, including frequency by sport, age and sex, as well as the percent of concussions within each sport were calculated. RESULTS: Out of a total of 56, 691 reported sports and recreational injuries, soccer accounted for the largest proportion of injuries with 11,941 reported cases over the 3 year time period. Of these, approximately 30% were fractures. The 10 - 14 year age group reported the greatest proportion of injuries in 10 out of the 13 sports analyzed. In addition, males reported a greater number of overall injuries than females in 11 out of the 13 sports analyzed. The largest percentage of concussions was reported in ringette; these injuries accounted for 17.1% of overall injuries within this sport. CONCLUSIONS: Injury prevention programs in Canada should focus on improving evidence-based programs to reduce the burden of injuries in all sports.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".