Lower Limb Movement Pattern Differences Between Males and Females in Squatting and Kneeling
Bibliographic record
Abstract
Movement pattern differences may contribute to differential injury or disease prevalence between individuals. The purpose of this study was to identify lower limb movement patterns in high knee flexion, a risk factor for knee osteoarthritis, and to investigate kinematic differences between males and females, as females typically develop knee osteoarthritis more commonly and severely than males. Lower extremity kinematic data were recorded from 110 participants completing 4 variations of squatting and kneeling. Principal component analysis was used to identify principal movements associated with the largest variability in the sample. Across the tasks, similar principal movements emerged at maximal flexion and during transitions. At maximal flexion, females achieved greater knee flexion, facilitated by a wider base of support, which may alter posterior and lateral tibiofemoral stress. Principal movements also detected differences in movement temporality between males and females. When these temporal differences occur due to alterations in movement velocity and/or acceleration, they may elicit changes in muscle activation and knee joint stress. Movement variability identified in the current study provides a framework for potential modifiable factors in high knee flexion, such as foot position, and suggests that kinematic differences between the sexes may contribute to differences in knee osteoarthritis progression.
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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 | 0.001 | 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) | 0.003 | 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".