226 Injuries in youth volleyball players at a national championship competition: incidence, risk factors and mechanism of injury
Bibliographic record
Abstract
Background Sport-related injuries present a substantial burden in youth sport. Injury surveillance data in youth volleyball is scarce. Understanding injury and concussion burden can inform prevention strategies. Objective To evaluate injury incidence rates, types, mechanism, and potential risk factors in youth volleyball. Design Prospective cohort study. Setting 2018 Canadian Youth National Volleyball Tournament. Participants All tournament players were invited to participate (9616 players). 1876 players [466 males, 1391 females, mean age 16.2 years (1.26)] consented to participate (19.5%). Assessment of Risk Factors Sex (male/female), age group (U14, U16, U18), level of play [elite (top 30%) vs. non-elite]. Main Outcome Measures Players completed a questionnaire (demographic information, injury, and concussion history). All medical attention injuries were recorded by tournament medical personnel via injury report form (e.g., mechanism, type). Injury was defined as any physical complaint seeking onsite medical attention. Concussion was defined using the 5th International Consensus Conference on Concussion in Sport. Exploratory multivariable Poisson regression was used to analyze potential risk factors (sex, age group, level of play) for injury, adjusted for cluster by team and offset by athlete-exposures (AEs). Results Of the 105 total injuries [6.15 injuries/1000 AEs (95% CI: 5.01 to 7.47)], concussion was the most common (n=28; 26.2%), followed by knee (n=16; 15.0%) and ankle injuries (n=15; 14.0%). Most concussions occurred due to ball-to-head contact (61.5%) and were unanticipated (84.6%). There was no difference in injury risk by sex (IRRF/M: 1.40; 95% CI: 0.73 to 2.66). Players in U18 had significantly lower rates of injury, compared to U16 and U14 (IRRU16: 2.44; 95% CI: 1.22 to 4.87; IRRU14: 3.58; 95% CI: 1.60 to 8.02). Conclusion Players in U18 had the lowest injury rates. More research is needed to elucidate why younger age groups reported more injuries and develop volleyball specific injury and concussion prevention strategies.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".