There Is No Relationship Between Lower Extremity Alignment During Unilateral and Bilateral Drop Jumps and the Risk of Knee or Ankle Injury: A Prospective Study
Bibliographic record
Abstract
Objective To investigate the association between lower extremity alignment during unilateral and bilateral drop jump tests and the risk of acute noncontact knee or ankle injuries in young team sport athletes. Design Prospective cohort study. Methods A 2-dimensional video analysis was used to measure the frontal plane knee projection angle in the single-leg vertical drop jump (VDJ) and the bilateral VDJ in young team sport athletes. Out of the 364 athletes (187 male, 177 female), 189 played basketball and 175 played floorball. Results Six male athletes sustained knee injuries and 23 sustained ankle injuries. Frontal plane knee projection angle in the single-leg VDJ or the bilateral VDJ was not associated with ankle injuries among male athletes. No statistical analysis was performed for the knee injuries. Among female athletes, 28 sustained knee and 41 sustained ankle injuries. Frontal plane knee projection angle during the single-leg VDJ or the bilateral VDJ was not a risk factor for knee or ankle injuries. Conclusion Lower extremity alignment during unilateral and bilateral drop jump tests was not associated with future noncontact knee or ankle injuries among young team sport athletes. The findings should be interpreted cautiously due to the small number of injuries. J Orthop Sports Phys Ther 2020;50(5):267–274. doi:10.2519/jospt.2020.9247
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".