684 FO39 – Skating on the edge of risk for female adolescent ice hockey players: a 5-year canadian longitudinal cohort study
Bibliographic record
Abstract
Background Ice hockey is a popular global sport with growing participation for boys and girls yet remains a high-risk injury sport. While evidence for risk factors including bodychecking policy have been well established, others such as sex and concussion history have been understudied to date. Objective To examine factors associated with game and practice-related injury rates in Canadian adolescent (ages 11–17) ice hockey. Design Prospective cohort (2013–2018). Setting Community ice hockey arenas. Participants 4418 male and female ice hockey players from all levels of play (6584 player-seasons) participating in under-13 (ages 11–12), under-15 (ages 13–14), and under-18 (ages 15–17) age groups. Assessment of Risk Factors Multilevel Poisson regression (adjusting for cluster by team and including multiple imputation for missing covariates) was used to estimate incidence rate ratios (IRRs) for sex, age group, bodychecking policy, year of play, level of play, weight, previous injury within last 12 months, lifetime concussion history, and position. Main Outcome Measurements All injuries (medical attention, inability to complete session, time-loss from playing) were identified using validated injury surveillance methodology. Results There were 1184 game-related and 182 practice-related injuries. Factors associated with game-related injury included female sex (IRR=1.57,95%CI; 1.18–2.08), previous injury (IRR=1.46,95%CI; 1.26–1.70), and lifetime concussion history (IRR=1.41,95%CI; 1.23–1.62). Goaltenders were protected (IRR=0.54,95%CI; 0.40–0.72) relative to forwards, as were players exposed to policy disallowing bodychecking in games (IRR=0.44,95%CI; 0.35–0.55). Female sex (IRR=1.90,95%CI; 1.10–3.28) and lifetime concussion history were also significantly associated with practice-related injury (IRR=1.53,95%CI; 1.08–2.18). Conclusions Several factors associated with injury rates in youth ice hockey were identified. In addition to a 56% lower rate of game-related injury in non-bodychecking leagues, girls and players with previous injury and concussion history experience the highest rates of injury. Future research examining female-specific injury prevention strategies in youth ice hockey is a priority.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".