Sex Differences in Lower Limb Proprioception and Mechanical Function Among Healthy Adults
Bibliographic record
Abstract
Twenty-four healthy adults, including 12 females and 12 males, participated in the study. Each female participant completed three trials in three different phases of one menstrual cycle, which included follicular, ovulatory, and luteal phases. The study aimed to investigate whether there is any difference in joint kinetic sense, neuromuscular coordination, and isokinetic muscle strength (a) between healthy males and females at different phases of the menstrual cycle and (b) between females at different phases of the menstrual cycle. The outcome measures included the number of jumps in the square-hop test and ankle and knee proprioception, which were assessed by an electric-driven movable frame rotated at 0.4 deg/s and isokinetic muscle strength measured by a computerized dynamometer (Biodex). For the square-hop test (p = .006), ankle dorsiflexion/plantar flexion (p < .05), knee flexion/extension (p < .05), the relative peak torque of the isokinetic muscle strength at the 60° and 180° knee flexion/extension (p < .001), and the 30° and 120° ankle plantar flexion/dorsiflexion (p < .05) between females and males showed significant differences. For the females at different phases of the menstrual cycle, significant differences were found on ankle dorsiflexion (p = .003), plantar flexion (p = .023), knee extension (p = .029), the square-hop test (p = .036), and relative peak torque of isokinetic muscle strength at 180° knee flexion (p = .029). This study demonstrated that there are sex differences in lower limb proprioception and mechanical function. Females at ovulatory and luteal phases have better lower limb proprioception than at the follicular phase.
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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) | 0.002 | 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".