461 Narrowing the gender gap in rugby injury epidemiology: a novel video-analysis study in the women’s game
Bibliographic record
Abstract
Background Rugby Union has one of the highest risks of injury and concussion in team sports globally. Despite growing participation, little data exists surrounding injury rates and mechanisms in women’s rugby. Objective To identify suspected injury and concussion events in women’s varsity rugby union Design Video-analysis study using validated suspected injury definition criteria. Setting University women’s rugby Patients (or Participants) Women’s ‘Canada West’ varsity rugby athletes (2017–2019 seasons) Interventions (or Assessment of Risk Factors) Video-analysis of game events leading to suspected injury and concussion Main Outcome Measurements Suspected injury and concussion based on content validation and consensus by nine rugby-specific researchers, therapists, and sport medicine physicians Results There were 225 suspected injuries recorded in 48 games (Injury rate (IR)= 115.1/1000 hours [95% CI;100.5–131.2] or 4.7 injuries per match). The on-field medical attention IR was 93.1/1000 hours (95% CI;80.1–107.6: 3.8 per game). Suspected concussions accounted for 26% of injuries (30.2/1000 hours: 95% CI;23.0–38.9: 1.2 per game). The attacking team sustained 64.0% of injuries. Removal from play was observed for 28.9% off suspected injuries. The most common injury locations were head/neck (28.4%) and lower extremity (27.6%). The tackle accounted for 67.1% of all injuries, with a propensity of 11.2/1000 tackle events (95% CI;9.5–13.2) or 3.1 tackle-related injuries/game. Of tackle-related injuries, 63.6% were to the ball carrier, while 52.2% of tackle-related concussions were to the ball carrier. Conclusions This study adds to the growing body of literature examining women’s rugby union. The rate of suspected injury is high compared with other rugby injury studies. It is acknowledged that suspected injuries and not supported by prospective injury surveillance. The high proportion of tackle-related suspected injuries warrants further investigation into specific characteristics which may be associated with injury onset, and in particular concussion.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".