Risk of injury and concussion associated with team performance and penalty minutes in competitive youth ice hockey
Bibliographic record
Abstract
OBJECTIVE: To determine if there is an association between the risk of all injury or concussion and win-loss records or penalty minutes in competitive youth ice hockey players (ages 11-14). DESIGN, SETTING, PARTICIPANTS: This is a secondary data analysis of a 2-year cohort study in Alberta and Quebec on the 2007/2008 (Pee Wee) and 2008/2009 (Bantam) seasons. Main outcome measures Incidence rate ratios (IRRs) were estimated based on Poisson regression for game-related injury and concussion and adjusted for cluster by team. RESULTS: A total of 140 teams from Alberta (n=2081) and 137 teams (n=2018) from Quebec were included in the analysis. There were 451 game-related injuries (121 concussions) from Alberta and 280 (62 concussions) from Quebec. For game-related injuries, the IRR between players from teams with more than 50% wins and players with less or equal to 50% wins was 0.78 (95% CI 0.64-0.95) for all injuries, 0.75 (95% CI 0.52-1.08) for concussions, 0.64 (95% CI 0.47-0.88) for injuries resulting in time loss of more than 7 days, and 0.74 (95% CI 0.39-1.40) for concussions resulting in time loss of more than 10 days; adjusting for clustering by team and other important risk factors (i.e., province, age, level of play, previous injury, weight and position). There was no association found between the total penalty minutes per game and game-related injury or concussion. CONCLUSIONS: There was a significant association found between team performance (i.e., win/loss/tie record) and injury risk with a 22% lower injury rate and 36% lower injury rate resulting in less than 7 days time loss in Pee Wee and Bantam ice hockey teams winning more than 50% of all season games. Total penalty minutes per game were not associated with injury or concussion rates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".