Injury Rates, Risk Factors, and Mechanisms of Injury in Minor Hockey
Bibliographic record
Abstract
BACKGROUND: Hockey is one of the top sports for participation in youth in Canada. There are limited data on the epidemiology of injury in youth hockey. PURPOSE: Through implementation and validation of an injury surveillance system, youth ice hockey injury rates, risk factors, and mechanisms of injury will be examined. STUDY DESIGN: Descriptive epidemiology study. METHODS: During the 2004-2005 season in minor hockey in Calgary, Alberta, Canada, 71 hockey teams (N = 986) were studied, including teams from each age group (Atom, 9/10 years; Pee Wee, 11/12 years; Bantam, 13/14 years; Midget, 15/16 years) and division of play (7-10 divisions per age group). A certified athletic therapist or candidate did weekly assessments of any identified hockey injury. Injury definition included any injury occurring during the regular hockey season that required medical attention, removal from a session, or missing a subsequent session. RESULTS: Of the 986 participating players, 216 players sustained a total of 296 injuries in the 2004-2005 season. The overall injury rate was 30.02 injuries per 100 players per season (95% confidence interval, 27.17-32.99) or 4.13 injuries per 1000 player hours (95% confidence interval, 3.67-4.62). Forty-five percent of all injuries occurred during body checking. Compared with the youngest age group, Atom, the risk of injury was greater in Pee Wee (relative risk, 2.97; 95% confidence interval, 1.63-5.8), Bantam (relative risk, 3.72; 95% confidence interval, 2.08-7.14), and Midget (relative risk, 5.43; 95% confidence interval, 3.14-10.17) leagues. The risk of injury in Pee Wee was greatest in the most elite divisions (relative risk, 2.45; 95% confidence interval, 1.15-5.81). Concussion, shoulder sprain/dislocation, and knee sprain were the most common injuries. CONCLUSION: Significant differences in injury rates were found by age and division of play. The public health significance of body checking injury in minor hockey is great. Future research will include expansion of surveillance to further examine body checking injuries and prevention strategies in minor hockey.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".