Association between frontal plane knee control and lower extremity injuries: a prospective study on young team sport athletes
Bibliographic record
Abstract
BACKGROUND/AIM: Poor frontal plane knee control can manifest as increased dynamic knee valgus during athletic tasks. The purpose of this study was to investigate the association between frontal plane knee control and the risk of acute lower extremity injuries. In addition, we wanted to study if the single-leg squat (SLS) test can be used as a screening tool to identify athletes with an increased injury risk. METHODS: A total of 306 basketball and floorball players participated in the baseline SLS test and a 12-month injury registration follow-up. Acute lower extremity time-loss injuries were registered. Frontal plane knee projection angles (FPKPA) during the SLS were calculated using a two-dimensional video analysis. RESULTS: Athletes displaying a high FPKPA were 2.7 times more likely to sustain a lower extremity injury (adjusted OR 2.67, 95% CI 1.23 to 5.83) and 2.4 times more likely to sustain an ankle injury (OR 2.37, 95% CI 1.13 to 4.98). There was no statistically significant association between FPKPA and knee injury (OR 1.49, 95% CI 0.56 to 3.98). The receiver operating characteristic curve analyses indicated poor combined sensitivity and specificity when FPKPA was used as a screening test for lower extremity injuries (area under the curve of 0.59) and ankle injuries (area under the curve of 0.58). CONCLUSIONS: Athletes displaying a large FPKPA in the SLS test had an elevated risk of acute lower extremity and ankle injuries. However, the SLS test is not sensitive and specific enough to be used as a screening tool for future injury risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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) | 0.002 | 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".