The Association of Age and Sex With Joint Angles and Coordination During Unanticipated Cutting in Soccer Players
Bibliographic record
Abstract
Deficits in movement patterns during cutting while running might place soccer players at risk of injury. The objective was to compare joint angles and intersegment coordination between sexes and ages during an unanticipated side-step cutting task in soccer players. This cross-sectional study recruited 11 male (four adolescents and seven adults) and 10 female (six adolescents and four adults) soccer players. Three-dimensional motion capture was used to measure lower-extremity joint and segment angles as participants performed an unanticipated cutting task. Hierarchical linear models examined relationships between joint angle characteristics with age and sex. Continuous relative phase was used to quantify intersegment coordination amplitude and variability. These values were compared between age and sex groups using analysis of covariance. Adult males had greater hip flexion angle excursions than adolescent males, while adult females had smaller excursions than adolescent females (p = .011). Females had smaller changes in hip flexion angles (p = .045), greater hip adduction angles (p = .043), and greater ankle eversion angles (p = .009) than males. Adolescents had greater hip internal rotation (p = .044) and knee flexion (p = .033) angles than adults, but smaller changes in knee flexion angles at precontact compared with stance/foot off (p < .001). For intersegment coordination, females were more out-of-phase than males in the foot/shank segment in the sagittal plane. There were no differences in intersegment coordination variability between groups. Differences in joint motion during an unanticipated cutting task were present between age groups and sexes. Injury prevention programs or training programs may be able target specific deficits to lower injury risk and improve performance.
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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 | 0.000 | 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) | 0.001 | 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".