Changing Hormone Levels during the Menstrual Cycle Affect Knee Laxity and Stiffness in Healthy Female Subjects
Bibliographic record
Abstract
BACKGROUND: Whether knee laxity varies throughout the menstrual cycle remains controversial. As increased laxity may be a risk factor for anterior cruciate ligament (ACL) injury, further research is warranted. HYPOTHESIS: Variation in estradiol and progesterone levels during the menstrual cycle influences knee laxity and stiffness. STUDY DESIGN: Case control study; Level of evidence, 3. METHODS: The serum estradiol and progesterone levels of 26 healthy female subjects were recorded in the follicular phase, ovulation, and the luteal phase. Knee joint laxity was assessed using a standard knee arthrometer at the same intervals. Stiffness changes in the load-displacement curve were determined. Hormone levels across the cycle were compared between responders and nonresponders, defined by whether changes in knee laxity at 89 N occurred. RESULTS: Greater laxity at 89 N during ovulation was observed (ovulation: 5.13 +/- 1.70 mm vs luteal: 4.55 +/- 1.54 mm, P = .012). In knee laxity testing at manual maximum load, greater laxity was noticed during ovulation (14.43 +/- 2.60 mm, P = .018), as compared with the follicular phase (13.35 +/- 2.53 mm). A reduction in knee stiffness of approximately 17% (ovulation: 12.48 +/- 5.46 N/mm vs luteal: 15.02 +/- 7.71 N/mm, P = .042) during ovulation was observed. However, there were no differences in hormone levels between responders and nonresponders at 89 N. CONCLUSION: Female hormone levels are related to increased knee joint laxity and decreased stiffness at ovulation. To understand subject variations in knee joint laxity during the menstrual cycle in female athletes, further investigation is warranted.
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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".