11.36 It takes two to tango: high rates of concussion in both ball carriers and tacklers in high school boys’ rugby
Bibliographic record
Abstract
Objective To describe injury and concussion incidence rates (IR) and mechanisms in boys high-school rugby union. Design Cohort Study. Setting Calgary (Canada) high-school boys’ rugby 2018 and 2019 seasons. Participants 429 boys (481 player-seasons) (ages 14–18; median height:180cm, weight:73kg). Interventions (or Assessment of Risk Factors) Injury rates by match vs. training. Outcome Measures All injuries (medical attention/time loss) including concussion (5th Consensus on Concussion in Sport). Main Results The match-injury IR was 57.9/1000 player-hours (95%CIs; 45.5–73.8) and training-injury IR was 2.5/1000 player-hours (95%CIs; 1.7–3.5). Fifty-one match-concussions (38% of match injuries) and 13 training-concussions (24% of training injuries) were reported. The match-concussion IR was 22.0/1000 player-hours (95%CIs; 15.9–30.4), while the training-concussion IR was 0.6/1000 player-hours (95%CIs; 0.3–1.2). Median days time-loss was 12 [interquartile range (IQR):6–20] for match-concussions and 14 (IQR:7–17) for training-concussions. The tackle was responsible for 76% of match and 68% of training-concussions. In matches, 41% of concussions were sustained by the ball carrier and 35% by the tackler. In training, 45% of concussions were sustained by the tackler and 23% by the ball carrier. The ruck was the next most reported mechanism of concussion in matches (12%) and training (8%). Conclusions Concussions IRs are high in high-school boys’ rugby and account for a substantial proportion of all injuries, with the highest proportion occurring from the tackle. The tackle should be the focus of concussion prevention efforts in matches. The ball carrier and the tackler are both at risk of concussion and prevention strategies should uniquely target both players in the tackle.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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".