Bodychecking experience and rates of injury among ice hockey players aged 15–17 years
Bibliographic record
Abstract
BACKGROUND: Although high rates of injury occur in youth ice hockey, disagreements exist about the risks and benefits of permitting bodychecking. We sought to evaluate associations between experience with bodychecking and rates of injury and concussion among ice hockey players aged 15-17 years. METHODS: We obtained data from a prospective cohort study of ice hockey players aged 15-17 years in Alberta who played in leagues that permitted bodychecking. We collected data over 3 seasons of play (2015/16-2017/18). We compared players based on experience with bodychecking (≤ 2 v. ≥ 3 yr), estimated using local and national bodychecking policy and region of play. We used validated methodology of ice hockey injury surveillance to identify all injuries related to ice hockey games and defined concussions according to the Consensus Statement on Concussion in Sport. RESULTS: We included 941 players who contributed to 1168 player-seasons, with 205 players participating in more than 1 season. Compared with players with 2 years or less of bodychecking experience, those with 3 or more years of experience had higher rates of all injury (adjusted incidence rate ratio [IRR] 2.55, 95% confidence interval [CI] 1.57-4.14), injury with more than 7 days of time loss (adjusted IRR 2.65, 95% CI 1.50-4.68) and concussion (adjusted IRR 2.69, 95% CI 1.34-5.42). INTERPRETATION: Among ice hockey players aged 15-17 years who participated in leagues permitting bodychecking, more experience with bodychecking did not protect against injury. This provides further support for removing bodychecking from 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".