11.24 Concussions in youth volleyball players at a national championship competition: incidence, risk factors and mechanism of injury
Bibliographic record
Abstract
Objective To evaluate the incidence, mechanism, and sex as a risk factor for concussion in youth volleyball players. Design Prospective cohort. Setting 2018 Canadian Youth National Volleyball Tournament. Participants All tournament players were invited to participate. 1876 players [466 males, 1,391 females, mean age 16.2 years (+/- 1.26)] consented to participate. Assessment of Risk Factors Sex (male/female), age category (U14 – U18) and level of play (Division I – V). Outcome Measures Players completed a questionnaire including demographic information, injury and concussion history. All medical attention injuries were recorded by tournament medical personnel using an injury report form (including mechanism and type of injury). Concussion was defined as per the 5th International Consensus Conference on Concussion in Sport. Poisson regression was used to analyze risk factors (e.g. sex, age category, level of play) for concussion, adjusted for cluster by team and offset by athlete-exposures (AEs). Main Results 107 injuries occurred during the six-day tournament (6.09 injuries/1000 AEs). The most common injury was concussion (n = 28; 26.17%) with a rate of 1.58 concussions/1000 AEs. Most concussions occurred due to ball-to-head contact (61.5%), followed by player-player contact (23.1%) and player-floor contact (15.4%). 84.6% of concussions that occurred due to contacts were unanticipated. There was no significant difference in risk of concussion by sex, adjusted by age category and level of play (IRR: 3.57; 95% CI: 0.91, 14.03); however, the point estimate suggests females may be at greater risk than males. Conclusions Most concussions occurred due to ball-to-head contact and were unanticipated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".