Risk factors for injury and severe injury in youth ice hockey: a systematic review of the literature
Bibliographic record
Abstract
OBJECTIVE: To identify risk factors for injury in youth ice hockey (ie, body checking, age, player position, player experience and level of play). STUDY DESIGN: Systematic review and meta-analysis. METHODS: A systematic review of the literature, including a meta-analysis component was completed. Ten electronic databases and the American Society for Testing and Materials Safety in Ice Hockey series (volumes 1-4) were systematically searched with strict inclusion and exclusion criteria to identify articles examining risk factors for injury in youth ice hockey. RESULTS: Participation in games, compared with practices, was associated with an increased risk of injury in all studies examined. Age, level of play and player position produced inconsistent findings. Body checking was identified as a significant risk factor for all injuries (summary rate ratio: 2.45; 95% CI 1.7 to 3.6) and concussion (summary odds ratio: 1.71; 95% CI 1.2 to 2.44). CONCLUSIONS: Findings regarding most risk factors for injury remain inconclusive; however, body checking was found to be associated with an increased risk of injury. Policy implications regarding delaying body checking to older age groups and to only the most elite levels requires further rigorous investigation.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".