Alterations to cell metabolism in connective tissues of the knee after ovariohysterectomy in a rabbit model: are there implications for the postmenopausal athlete?: Table 1
Bibliographic record
Abstract
BACKGROUND: Participation in regular exercise and athletic activities across the lifespan is encouraged to maintain the cardiovascular and musculoskeletal systems and general wellbeing. Before the menopause there is an increased risk of anterior cruciate ligament (ACL) injuries in female athletes, whereas there is an increased risk of joint diseases such as knee osteoarthritis after the menopause. Although there are few data regarding alterations in individual connective tissues of the knee in humans either before, during or after the menopause, it is possible to assess changes in experimental models following surgical menopause. OBJECTIVE: To assess changes in cell metabolism in the medial collateral ligament, ACL, patellar tendon, lateral and medial menisci, tibial plateau and femoral condyle articular cartilage and the synovium after surgical menopause in an experimental model system. METHODS: Panels of rabbits were subjected to ovariohysterectomy or sham operations, and RNA from each tissue was assessed for collagen, proteoglycan, proteinase, growth factor, sex hormone receptor and inflammatory mediator messenger RNA levels by reverse transcribed PCR. RESULTS: Unique alterations in cell metabolism were detected 2 months after surgical menopause and the pattern of significant changes was tissue specific (number of mRNA species altered, extent of changes, elevation/depression of changes). CONCLUSIONS: Changes in cell metabolism may alter the set point for the tissues of the knee and subsequently the functioning of the knee after the menopause. Such changes may contribute to an increased risk of injury and/or degenerative conditions. Further studies in pre and postmenopausal women athletes may also shed light on whether the present findings can be extrapolated to human populations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".