Injury Rates and Profiles in Female Ice Hockey Players
Bibliographic record
Abstract
BACKGROUND: Little data exist on injury rates and profiles in female ice hockey players. OBJECTIVE: To examine the incidence of injury in female ice hockey players and compare injury rates with those of male players. STUDY DESIGN: Prospective cohort study. METHODS: Six male and six female teams from the Canada West Universities Athletic Association were followed prospectively for one varsity season. Preseason medical history forms were completed by each player. Injury report forms and attendance records for each team session were submitted by team therapists. RESULTS: Male players reported 161 injuries, whereas female players reported 66 injuries. However, the overall injury rates for male (9.19 injuries per 1000 athlete-exposures) and female (7.77 injuries per 1000 athlete-exposures) players did not differ significantly. Ninety-six percent of injuries in female players and 79% in male players were related to contact mechanisms, even though intentional body checking is not allowed in female ice hockey. Women were more likely than men to be injured by contacting the boards or their opponent. Men sustained more severe injuries than women and missed about twice as many sessions (exposures) because of injury. Concussions were the most common injury in female players, followed by ankle sprains, adductor muscle strains, and sacroiliac dysfunction. CONCLUSION: Although the injury rate in female ice hockey players was expected to be lower than that in male players because of the lack of intentional body checking, the injury rates were found to be similar.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".