JUMPER'S KNEE: A PROSPECTIVE EVALUATION OF RISK FACTORS IN VOLLEYBALL PLAYERS USING AN OVERUSE MEASURE OF INJURY
Bibliographic record
Abstract
Background A multitude of risk factors have been reported to increase an athlete's risk of developing jumper's knee. An overuse injury definition has been cited as more sensitive in capturing knee injuries when compared to a time-loss injury definition. To date, risk factors for jumper's knee have not been assessed for the development of knee problems captured by an overuse injury definition. Objective To assess a multitude of potential intrinsic risk factors for jumper's knee in volleyball players. Design Prospective Cohort. Setting Collegiate and national team training facilities. Patients (or Participants) Sixty elite adult male volleyball players were recruited from collegiate and national team programs in Canada. Interventions (or Assessment of Risk Factors) Participants completed risk factor assessments at the commencement of the season including: vertical jump ability (cm), weight bearing ankle dorsiflexion range (degrees), dynamic balance (cm), dynamic knee alignment (degrees) and landing mechanics (degrees). Main Outcome Measurements Self-reporting knee problems utilizing an overuse injury questionnaire collected via short message service (SMS) was completed prospectively over one season. Substantial knee problems were identified and logistic regression was used to estimate odds ratios for each risk factor independently. Results The season prevalence of knee problems was 75.0% (95% CI: 62.2 to 84.6) and the incidence proportion of those rated as substantial was 30.0% (95% CI: 19.5 to 43.1). The SMS system of tracking overuse injuries demonstrated 98.2% completeness. No single risk factor was found to predict substantial knee problems. All odds ratios were close to unity with narrow confidence intervals (0.91–1.07) and p>0.05. Conclusions A more sensitive capture of overuse knee injuries did not result in the identification of distinct risk factors for the development of jumper's knee. These findings bring question to the utility of field based pre-season risk factor assessment for the identification of at risk athletes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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".