Concussion incidence and mechanism among youth volleyball players
Bibliographic record
Abstract
Objective To assess the incidence of concussions among adolescent volleyball players in Canada. Design Cross sectional survey. Setting Online survey. Participants In total, 663 registered Volleyball Canada members completed a national survey, with a response rate of 13.0%; web-based survey response rates rarely exceed 5%. The 452 (68.2%) female and 211 (31.8%) male responders had a mean age of 16.2 (95% CI: 16.1to 16.4) years with a range from 14 to 19 years. Assessment of risk factors The type of environment: controlled non-competitive environment (practice or warm-up) versus competitive game play environment. Outcome measures Whether the athletes sustained a concussion as defined by the 2012 Zurich Consensus. Main results A total of 86 concussions were reported, of which 52 were in the previous 12 months, yielding a one-year cumulative incidence per 100 athletes of 7.1 (95% CI: 4.3 to 11.4) and 7.5 (95% CI: 5.4 to 10.3) for males and females, respectively. In total, 57.1% (95% CI: 46.2 to 67.5) of all concussions involved ball-to-head contact. Player-to-player contact and head-to-floor contact were less prevalent at 20.2% (95% CI: 12.8 to 30.4) and 15.5% (95% CI: 9.1 to 25.1) respectively. Practice environment accounted for 46.5% of all concussions while 38.4% occurred in game play. The remaining 15.1% occurred in warm-up. In total, 61.6% (95% CI: 50.2 to 71.7) of concussions occurred outside of free-flow competitive game play, in a more structured environment. Conclusions There is a significant margin for injury prevention as a substantial proportion (61.6%) of concussions happening in a noncompetitive, controlled environment that may be amenable to change that would reduce the potential for such injury. Competing interests The authors have no competing interest to state.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".