Postural Control as a Risk Factor for Noncontact Anterior Cruciate Ligament Injury in Youth Female Basketball and Floorball Athletes
Bibliographic record
Abstract
The aim of this study was to investigate whether postural control was associated with an increased risk of future noncontact ACL injury in youth female basketball and floorball athletes. Data collection on 189 youth female basketball and floorball athletes was performed during a 3-year period. The modified Star Excursion Balance Test (mSEBT), single-leg drop-down test, and single-leg stance tests on a balance platform were used to measure postural control. In the mSEBT, performance in the anteromedial, medial, and posteromedial directions, as well as the corresponding composite score, were recorded. In balance platform tests, the mediolateral and anteroposterior velocity, velocity moment, and side length of a square representing 90% of postural sway were measured. Relative limb asymmetry and bilateral limb mean results for these variables were calculated and used as predictor variables in Cox regression analysis. Noncontact ACL injuries and individual exposure hours were prospectively recorded throughout the follow-up. Twelve noncontact ACL injuries occurred. Greater limb asymmetry in the posteromedial direction [HR 1.18 (95% CI 1.05-1.32)] and composite score [HR 1.17 (95% CI 1.01-1.36)] on the mSEBT were associated with an increased risk of noncontact ACL injury. No statistically significant associations were found in the other directions for the mSEBT or any of the balance-platform-generated variables. Dynamic postural control, measured by limb asymmetry in the mSEBT, was associated with future ACL injury. Prevention programs for noncontact ACL injury could benefit from exercises directed toward correcting limb asymmetries in dynamic postural control in youth female basketball and floorball 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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".