No association found between body checking experience and injury or concussion rates in adolescent ice hockey players
Bibliographic record
Abstract
OBJECTIVES: To compare rates of injury and concussion among U-15 (ages 13-14 years) ice hockey players playing in leagues allowing body checking, but who have a varying number of years of body checking experience. METHODS: This 5-year longitudinal cohort included U-15 ice hockey players playing in leagues where policy allowed body checking. Years of body checking experience were classified based on national/local body checking policy. All ice hockey game-related injuries were identified using a validated injury surveillance methodology. Players with a suspected concussion were referred to a study sport medicine physician. Multiple multilevel Poisson regression analysis was performed, adjusting for important covariates and a random effect at a team level (offset by game exposure hours), to estimate injury and concussion incidence rate ratios (IRRs). RESULTS: In total, 1647 players participated, contributing 1842 player-seasons (195 players participating in two seasons). Relative to no body checking experience, no significant differences were found in the adjusted IRRs for game-related injury for players with 1 year (IRR=1.06; 95% CI: 0.77 to 1.45) or 2+ years (IRR=1.16; 95% CI: 0.74 to 1.84) body checking experience. Similarly, no differences were found in the rates of concussion for players with 1 year (IRR=0.92; 95% CI: 0.59 to 1.42) or 2+ years (IRR=0.69; 95% CI: 0.38 to 1.25) body checking experience. CONCLUSIONS: Among ice hockey players aged 13-14 years participating in leagues permitting body checking, the adjusted rates of all injury and concussion were not significantly different between those that had body checking experience and those that did not. Based on these findings, no association was found between body checking experience and rates of injury or concussion specifically in adolescent 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".