11.10 Factors associated with concussion rates in youth ice hockey players: data from the largest longitudinal cohort study in Canadian youth ice hockey
Bibliographic record
Abstract
Objectives To examine factors associated with rates of game-related concussion in youth ice hockey. Design Five-year prospective cohort. Setting Canadian ice hockey rinks. Participants 4419 male and female ice hockey players (6585 player-seasons) participating in Under-13 (ages 11–12), Under-15 (ages 13–14), and Under-18 (ages 15–17) age groups were recruited. Assessment of Risk Factors Body checking policy, age group, year of play, level of play, lifetime concussion history, sex, player weight, and position of play. Outcome Measures All game-related concussions were identified using validated injury surveillance methodology. Players with a suspected concussion were referred to a study sport medicine physician for diagnosis and management. Main Results Crude concussion rates were 1.82 concussions/1000 game-hours (95% CI: 1.44–2.30) for Under-13s, 3.47 (95% CI: 3.00–4.02) for Under-15s, and 3.61 (95% CI: 3.06–4.27) for Under-18s. Based on multiple multilevel Poisson regression analysis including multiple imputation of missing covariates, female players (IRRFemale/Male=1.72; 95% CI: 1.21–2.46) and players with a previous concussion history (IRR=1.81; 95% CI: 1.51–2.17) had higher rates of game-related concussion. Policy disallowing body checking in games (IRR=0.55; 95% CI: 0.41–0.73) and being a goaltender (IRRGoaltenders/Forwards=0.64; 95% CI: 0.44–0.95) were protective against game-related concussion. Conclusions In the largest Canadian youth ice hockey longitudinal cohort study to date, female players (despite policy disallowing body checking) and players with a concussion history had higher rates of concussion. Goalies and players in leagues where policy disallowed body checking had lower rates of concussion. Policy prohibiting body checking continues to be the most effective concussion prevention strategy in youth ice 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".