12.5 Female players are at the greatest risk of concussion and recurrent concussion in youth ice hockey: time for a focus on concussion prevention for female players
Bibliographic record
Abstract
Objective Examine factors associated with concussion in youth ice hockey players and re-examine these factors in players with a preseason concussion history in sustaining a recurrent concussion. Design Canadian ice hockey rinks. Setting A total of 4419 hockey players (male and female; ages 11–18) were recruited over 5 seasons generating 6585 player-seasons. Participants A total of 4419 hockey players (male and female; ages 11–18) were recruited over 5 seasons generating 6585 player-seasons. Interventions (or Assessment of Risk Factors) Sex, body-checking policy, age group, division of play, player position, and preseason history of concussion. Outcome Measures Hockey related (game or practice) concussions identified through validated injury surveillance. Main Results In a multiple failure time Cox regression adjusting for clustering by team, females were 90% more likely to sustain a concussion during the season [hazard ratio (HR)=1.90,95% CI:1.29–2.78], irrespective of playing in non-body-checking leagues. Body-checking policy was associated with a 78% increased hazard of sustaining a concussion (HR=1.78,95% CI:1.36–2.34) and preseason history of concussion also increased the hazard of concussion 60% during the season (HR=1.60, 95% CI:1.29–1.98). Of those with a preseason history of concussion, females remained at 81% higher risk of sustaining a recurrent concussion (HR=1.81,95% CI:1.08–3.03). Defensive players also saw an increase in the hazard of sustaining a recurrent concussion (HR=1.35, 95% CI:1.01–181). Conclusions Risk factors for concussion in youth ice hockey include being female, playing in a league that permits body-checking, and having a previous history of concussion. Among those with a preseason concussion history, females and defensive players were at higher risk of recurrent concussion. Female-specific concussion prevention strategies in youth ice hockey should be a focus of future research to reduce concussion and recurrent concussion risk.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.012 | 0.001 |
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".