Shoulder Check: Investigating Shoulder Injury Rates, Types, Severity, Mechanisms, and Risk Factors in Canadian Youth Ice Hockey
Bibliographic record
Abstract
OBJECTIVE: To describe shoulder-related injury rates (IRs), types, severity, mechanisms, and risk factors in youth ice hockey players during games and practices. DESIGN: Secondary analysis of data from a 5-year prospective cohort study, Safe-to-Play (2013-2018). SETTING: Canadian youth ice hockey. PARTICIPANTS: Overall, 6584 player-seasons (representing 4417 individual players) participated. During this period, 118 shoulder-related games and 12 practice injuries were reported. ASSESSMENT OF RISK FACTORS: An exploratory multivariable mixed-effects Poisson regression model examined the risk factors of body checking policy, weight, biological sex, history of injury in the past 12 months, and level of play. MAIN OUTCOME MEASURES: Injury surveillance data were collected from 2013 to 2018. Injury rates with 95% confidence interval (CI) were estimated using Poisson regression. RESULTS: The shoulder IR was 0.35 injuries/1000 game-hours (95% CI, 0.24-0.49). Two-thirds of game injuries (n = 80, 70%) resulted in >8 days of time-loss, and more than one-third (n = 44, 39%) resulted in >28 days of time-loss. An 83% lower rate of shoulder injury was associated with policy prohibiting body checking compared with leagues allowing body checking (incidence rate ratio [IRR], 0.17; 95% CI, 0.09-0.33). A higher shoulder IR was observed for those who reported any injury in the last 12-months compared with those with no history (IRR, 2.00; 95% CI, 1.33-3.01). CONCLUSIONS: Most shoulder injuries resulted in more than 1 week of time-loss. Risk factors for shoulder injury included participation in a body-checking league and recent history of injury. Further study of prevention strategies specific to the shoulder may merit further consideration in 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 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".