703 FO01 – From rink to prevention and recovery: investigating upper extremity injuries in Canadian youth ice hockey
Bibliographic record
Abstract
Background Youth ice hockey is a popular, fast-paced, collision sport with a high participation rate. Objective To describe upper extremity (UE) injury rates, types, severity, mechanisms, and risk factors in youth ice hockey players. Design Secondary analysis of data from a 5-year prospective cohort study (2013–2018). Setting Canadian youth ice hockey. Participants Overall, 6584 player-seasons (representing 4418 individual players) participated. Assessment of Risk Factors A multivariable mixed-effects Poisson regression model (clustering by team and offset by exposure hours) examined potential risk factors for UE injury including body checking policy, player weight, biological sex, history of injury in the past 12 months, and level of play. Main Outcome Measures Validated injury surveillance data was collected. The injury outcome for this analysis consisted of primary upper extremity injuries. Injury rates (IR) with 95% CI were estimated using Poisson regression. Results During this period, 234 UE game-related and 35 practice-related injuries were reported. The UE IR was 0.91 injuries/1000 game-hours (95% CI 0.78–1.08). Most injuries (n=188/234, 71%) resulted in >7 days of time-loss and 106/188 (40%) resulted in >28 days of time-loss. An 83% lower IR was associated with policy prohibiting bodychecking compared to leagues allowing bodychecking [IRR=0.17 (95% CI 0.09–0.29)]. A higher upper extremity IR was seen for those who reported any injury in the last 12-months compared to those with no history [IRR=1.85 (95% CI 1.30–2.55)]. There were no associations found between UE IR and player weight, biological sex, or level of play. Conclusions UE injuries typically result in >7 days time-loss and 40% >28 days. Risk factors for UE injury included participation in a body checking league, and recent history of any injury. This study serves to reinforce the need for the investigation of UE injury prevention strategies to make youth ice hockey a safer space.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".