Hormonal modulation of connective tissue homeostasis and sex differences in risk for osteoarthritis of the knee
Bibliographic record
Abstract
Young female athletes experience a higher incidence of ligament injuries than their male counterparts, females experience a higher incidence of joint hypermobility syndrome (a risk factor for osteoarthritis development), and post-menopausal females experience a higher prevalence of osteoarthritis than age-matched males. These observations indicate that fluctuating sex hormone levels in young females and loss of ovarian sex hormone production due to menopause likely contribute to observed sex differences in knee joint function and risk for loss of function. In studies of osteoarthritis, however, there is a general lack of appreciation for the heterogeneity of hormonal control in both women and men. Progress in this field is limited by the relatively few preclinical osteoarthritis models, and that most of the work with established models uses only male animals. To elucidate sex differences in osteoarthritis, it is important to examine sex hormone mechanisms in cells from knee tissues and the sexual dimorphism in the role of inflammation at the cell, tissue, and organ levels. There is a need to determine if the risk for loss of knee function and integrity in females is restricted to only the knee or if sex-specific changes in other tissues play a role. This paper discusses these gaps in knowledge and suggests remedies.
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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 | 0.000 | 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".