Jumper's Knee: A Prospective Evaluation of Risk Factors in Volleyball Players Using a Novel Measure of Injury
Bibliographic record
Abstract
OBJECTIVES: To examine potential intrinsic risk factors that may contribute to the onset of jumper's knee in elite level-male volleyball players. DESIGN: Prospective Cohort Study. SETTING: Varsity and National team volleyball gymnasiums. PARTICIPANTS: Sixty elite adult male volleyball players from Canada. ASSESSMENT OF RISK FACTORS: Players completed a series of risk factor assessments at the commencement of their seasons, including vertical jump (cm), ankle dorsiflexion range (degrees), dynamic balance (normalized distance reached; cm), dynamic knee alignment (degrees), and landing mechanics (degrees). MAIN OUTCOME MEASURE: Self-reported knee problems, captured via short message service. RESULTS: Knee problem prevalence was 75% [95% confidence intervals (CIs): 62.2-84.6] and the incidence rate for substantial injuries over the study period was 30 injuries/100 players/season (95% CI: 19.5-43.1). No risk factor was found to significantly predict the future occurrence of developing jumper's knee. The odds ratios were close to unity (range: 0.94-1.07) with narrow confidence intervals and P > 0.05. CONCLUSIONS: A more sensitive capture of overuse knee problems did not result in the identification of distinct risk factors for the development of jumper's knee. These findings highlight a lack of available methodology to accurately assess risk factors for overuse injuries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".