810 MEP024 – Male and female basketball related injuries at the Canada games in 2009, 2013, and 2017
Bibliographic record
Abstract
Background The Canada Games (CG) is the largest sporting event for young Canadian athletes. Understanding injury differences between male and female athletes is integral for injury prevention and treatment. Objective To compare injury rates between male and female basketball players competing at the CG. Design Retrospective cohort. Setting Canada Summer Games Participants All male and female basketball participants registered to compete in the 2009, 2013 and 2017 Canada Games. Main Outcome Measurements Injuries related to participation were identified based on coding medical reports using the 2020 International Olympic Committee consensus statement on methods for recording and reporting epidemiological data on injury and illness in sport. Incidence proportion (injuries per 100 registered athletes) and incidence rates (injuries per 1000 athlete-days) with 95% confidence intervals (CI). Crude incidence rate ratios (IRR; 95% CI) were calculated to compare male and female injury rates. Results In total, 793 athletes (382 females, 411 males, ages 14–18), participated in a 6-day (2013) or 7-day (2009, 2017) event across three CG. There were 84 female injuries [22.0 injuries per 100 athletes (95% CI: 17.4–27.2) or 33.0 injuries per 1000 athlete-days (26.3–40.8) and 80 male injuries [19.46 per 100 athletes (14.43–24.22) or 29.19 per 1000 athlete-days (23.1–36.3)]. No significant difference between male and female injury rates was observed [IRR: 1.13 (0.82–1.55)]. Contact injuries were common, with 44 female [11.5 per 100 athletes (8.4–15.5)] and 42 male cases [10.2 per 100 athletes (7.4–13.8)]. Non-contact injuries included 16 female [4.2 per 100 athletes (2.4–6.8) and 21 male cases [5.1 per 100 athletes (3.2–7.8)]. The most prominent injury locations for females and males were the ankle (23% and 19%, respectively), and knee (14% and 15%, respectively). Conclusion Both male and female athletes sustained approximately 1 in 5 injuries at CG. Lower body and contact injuries were prominent among CG basketball athletes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".