Body checking in non-elite adolescent ice hockey leagues: it is never too late for policy change aiming to protect the health of adolescents
Bibliographic record
Abstract
OBJECTIVES: The objective of this study is to evaluate the effect of policy change disallowing body checking in adolescent ice hockey leagues (ages 15-17) on reducing rates of injury and concussion. METHODS: This is a prospective cohort study. Players 15-17 years-old were recruited from teams in non-elite divisions of play (lower 40%-70% by division of play depending on year and city of play in leagues where policy permits or prohibit body checking in Alberta and British Columbia, Canada (2015-18). A validated injury surveillance methodology supported baseline, exposure-hours and injury data collection. Any player with a suspected concussion was referred to a study physician. Primary outcomes include game-related injuries, game-related injuries (>7 days time loss), game-related concussions and game-related concussions (>10 days time loss). RESULTS: 44 teams (453 player-seasons) from non-body checking and 52 teams (674 player-seasons) from body checking leagues participated. In body checking leagues there were 213 injuries (69 concussions) and in non-body checking leagues 40 injuries (18 concussions) during games. Based on multiple multilevel mixed-effects Poisson regression analyses, policy prohibiting body checking was associated with a lower rate of injury (incidence rate ratio (IRR): 0.38 (95% CI 0.24 to 0.6)) and concussion (IRR: 0.49; 95% CI 0.26 to 0.89). This translates to an absolute rate reduction of 7.82 injuries/1000 game-hours (95% CI 2.74 to 12.9) and the prevention of 7326 injuries (95% CI 2570 to 12083) in Canada annually. CONCLUSIONS: The rate of injury was 62% lower (concussion 51% lower) in leagues not permitting body checking in non-elite 15-17 years old leagues highlighting the potential public health impact of policy prohibiting body checking in older adolescent ice hockey players.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".