Relationship between knee joint laxity and knee joint mechanics during the menstrual cycle
Bibliographic record
Abstract
BACKGROUND: An increase in knee laxity during the menstrual cycle may increase the risk of anterior cruciate ligament injury. OBJECTIVE: To investigate whether changing knee laxity during the menstrual cycle correlates with changing knee joint loads in a cutting manoeuvre. DESIGN: Cross-sectional study. SETTING: Laboratory testing. PARTICIPANTS: 25 healthy women, with a normal menstrual cycle, no history of oral contraceptive use, and no previous knee injury INTERVENTIONS: Serum hormone concentrations were assessed and knee joint laxity at a load of 89 N was measured during the follicular, ovulation and luteal phases. Participants performed 10 trials of a cutting manoeuvre to quantify knee joint mechanics at each test session. MAIN OUTCOME MEASUREMENTS: Knee joint laxity (mm), peak knee angle ( degrees ) and knee joint moment (Nm) and knee joint impulse (Nms). RESULTS: Increased knee laxity was observed during ovulation compared with the luteal phase, but no significant changes in knee mechanics corresponding to menstrual phases were found. A positive correlation was found between changes in knee laxity and changes in knee joint loads (Deltamoment or Deltaimpulse) from the follicular phase to ovulation, and from ovulation to the luteal phase (p<0.05). Women in whom knee laxity increased showed increased knee loads, and those in whom knee laxity decreased showed decreased knee loads during the menstrual cycle. CONCLUSIONS: Knee laxity correlates positively with knee joint loads, and increased knee laxity during the menstrual cycle may be a potential risk factor for anterior cruciate ligament injuries in certain women during sports activity.
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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 | 0.004 |
| 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".