11.30 More than meets the eye: concussion rates and mechanisms informing sex-specific concussion prevention strategies in high-school rugby union
Bibliographic record
Abstract
Objective To compare injury and concussion rates in female and male high-school rugby players. Design Cohort study. Setting Rugby pitches (Calgary, Canada). Participants 361 female (421 player-seasons) and 421 male (481 player-seasons) high-school rugby players over two playing-seasons (2018 and 2019). Interventions (or Assessment of Risk Factors) Male and female injury and concussion incidence rates (IR) and tackle-specific IR comparisons were made. Outcome Measures Match-injuries (medical attention/time loss) and concussions (5th Consensus on Concussion in Sport) were recorded with referral to physician for suspected concussion. Main Results The match-IR was 62% higher (Incidence Rate Ratio [IRR]=1.62; 95% CI: 1.20–2.18) for females (IR=93.68/1000 match-hours; 95% CI: 78.57–111.71) than males (IR=57.86; 95% CI: 45.35–73.8). Concussion was the most common match-injury for females (40%) and males (38%). The female match-concussion IR (37.47/1000 match-hours; 95% CI: 26.83–52.34) was 70% higher than for males (22.02/1000 match-hours; 95% CI: 15.94–30.43) (IRR=1.70; 95% CI: 1.08–2.69). Explicitly considering the tackle event, the ball carrier-related match-concussion IR for females (IR=11.48; 95% CI: 6.95–18.96) did not differ from males (IR=9.07; 95% CI: 5.50–14.94) (IRR = 1.27; 95% CI: 0.63–2.54). Although not statistically significant, the tackler-related concussion IR was higher for females (IR=18.13; 95% CI: 11.48–28.63) than males (IR=7.77; 95% CI: 3.68–16.41) (IRR = 2.33; 95% CI: 0.98–5.53). Conclusions Injury and concussion rates are higher for females than males in this Canadian cohort. Video-analysis could inform a greater understanding of the mechanisms of injury within the tackler and ball-carrier events leading to prevention strategies in youth rugby.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".