The relationship between executed cut angle and speed with lower extremity joint angles during unanticipated side-step cutting in soccer players
Bibliographic record
Abstract
BACKGROUND: Certain movements patterns have been indicated in knee injuries during cutting while running tasks. Differences in the executed cut angle (ECA) could partially account for group differences in joint kinematics previously observed, including sex differences. RESEARCH QUESTION: Are there relationships between joint angles with entry speed and ECA during side-step cutting in soccer players? METHODS: This cross-sectional study recruited 21 (10 females) soccer players. Participants completed 45° unanticipated side-step cuts in both directions. Kinematic data were captured with a 12 camera motion capture system with 46 reflective markers placed on the participants. Peak joint angles were determined during the stance phase of the cutting task. Entry speed and ECA were determined from pelvis markers. Hierarchical linear models examined relationships between angles, entry speed, and ECA, after accounting for age, sex, and leg preference. Regression coefficients with 95 % confidence intervals were reported and statistical significance (p<0.05) were examined using the Wald statistic. RESULTS: The mean ECA (24.6°) was lower than the intended 45° angle. Peak joint angles were significantly related to both ECA and entry speed. Specifically, an increase in ECA by 10° (i.e., sharper cuts) would increase hip internal rotation and ankle plantarflexion by 1.8-2.1°, and decrease hip adduction, knee abduction and ankle eversion by 1.3-2.4°. Faster entry speeds by 0.5 m/s would increase hip flexion, hip internal rotation and knee extension angles by 1.8-3.8°, and decrease knee abduction by 2.6°. SIGNIFICANCE: Studies evaluating cutting while running should consider ECA and entry speed in their design. Potential differences could confound between-group comparisons of joint angles, including when comparing sexes, and impact interpretations of 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".