Injury rates, types, mechanisms and risk factors in female youth ice hockey
Bibliographic record
Abstract
BACKGROUND: The objectives of this cohort study were to examine the rate, types, mechanisms and risk factors for injury in female youth (ages 9-17) ice hockey players in the Girls Hockey Calgary Association. METHODS: The main outcome was ice hockey injury, defined as any injury occurring during the 2008/2009 season that required medical attention, and/or removal from a session and/or missing a subsequent session. Potential risk factors included age group, level of play, previous injury, ice hockey experience, physical activity level, weight, height, position of play and menarche. Incidence rate ratios (IRR) were estimated with Poisson Regression adjusted for cluster (team). Exposure data were collected for every session for each participating player. RESULTS: Twenty-eight teams (n=324) from Atom (ages 9-10), PeeWee (11-12), Bantam (13-14) and Midget (15-17) participated with 53 reported injuries. The overall injury rate was 1.9 injuries/1000 player-hours (95% CI 1.4 to 2.7). Previous injury (IRR=2.7, 95% CI 1.7 to 4.3), games (IRR=2.1, 95% CI 1.1 to 4.2), menarche (PeeWee) (IRR=4.1, 95% CI 1.0 to 16.8) were significant risk factors. In Midget, the more elite divisions were associated with a lower injury risk (A-IRR=0.2, 95% CI 0.1 to 0.5) (AAA-IRR=0.5, 95% CI 0.2 to 0.9). CONCLUSIONS: Injury rates were lower in this study than previously found in male youth and women's ice hockey populations. Previous injury and game play as risk factors are consistent with the literature. Menarche as a risk factor is a new finding in this study. This research will inform future studies of the development of injury prevention strategies in this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".