Alterations in Knee Joint Laxity During the Menstrual Cycle in Healthy Women Leads to Increases in Joint Loads During Selected Athletic Movements
Bibliographic record
Abstract
BACKGROUND: It has been speculated that the hormonal cycle may be correlated with higher incidence of ACL injury in female athletes, but results have been very contradictory. HYPOTHESIS: Knee joint loads are influenced by knee joint laxity (KJL) during the menstrual cycle. STUDY DESIGN: Controlled laboratory study. METHODS: Serum samples and KJL were assessed at the follicular, ovulation, and luteal phases in 26 women. Knee joint mechanics (angle, moment, and impulse) were measured and compared at the same intervals. Each of the 26 subjects had a value for knee laxity at each of the 3 phases of their cycle, and these were ordered and designated low, medium, and high for that subject. Knee joint mechanics were then compared between low, medium, and high laxity. RESULTS: No significant differences in knee joint mechanics were found across the menstrual cycle (no phase effect). However, an increase in KJL was associated with higher knee joint loads during movement (laxity effect). A 1.3-mm increase in KJL resulted in an increase of approximately 30% in adduction impulse in a cutting maneuver, an increase of approximately 20% in knee adduction moment, and a 20% to 45% increase in external rotation loads during a jumping and stopping task (P < .05). CONCLUSION: Changes in KJL during the menstrual cycle do change knee joint loading during movements. Clinical Relevance Our findings will be beneficial for researchers in the development of more effective ACL injury prevention programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".