Lower extremity energy absorption strategies at different phases during single and double-leg landings with knee valgus in pubertal female athletes
Bibliographic record
Abstract
Dynamic knee valgus (DKV) malalignment affects the biomechanical characteristic during sports activities. This cross-sectional study was conducted to evaluate mechanical energy absorption (MEA) strategies at initial contact (IC) and total landing (TL) phases during single-leg landing (SLL), and double-leg landing (DLL). Twenty-eight female athletes with DKV (age 10-14) were invited. MEA analysis of lower extremity joints was done in sagittal and frontal motion planes employing 8 Vicon motion capture cameras and 2 Kistler force plates. Statistical analysis was done using IBM Statistics (version24) by Bivariate Pearson Correlation Coefficient test. Knee extensors MEA during SLL (IC: P = 0.008, R = 0.522/TL: P < 0.001, R = 0.642) and DLL (IC: P < 0.001, R = 0.611/TL: P = 0.011, R = 0.525), and knee abductors during SLL (IC: P = 0.021, R = 0.474) were positively correlated with increased DKV angle. Ankle plantar flexors during SLL (TL: P = 0.017, R = - 0.477) and DLL (TL: P = 0.028, R = - 0.404), and hip extensors during SLL (TL: P = 0.006, R = - 0.5120) were negatively correlated with increased DKV angle. Compensated MEA in knee extensors was correlated with less ankle plantar flexion MEA during SLL (IC: P = 0.027, R = - 0.514/TL: P = 0.007, R = - 0.637) and DLL (IC: P = 0.033, R = - 00.412/TL: P = 0.025, R = - 0.485). These outcomes indicated a knee-reliant MEA strategy in female athletes with DKV during puberty, putting them at higher risks of ACL injuries during landing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".