Meta-Analysis of Estrogen in Osteoarthritis: Clinical Status and Protective Effects.
Bibliographic record
Abstract
Context: Osteoarthritis (OA) impacted over 5-million people worldwide in 2018, with an incidence second only to diabetes and hypertension. Clinical research has had difficulty in finding methods to treat OA quickly and effectively. More and more researchers have begun to explore the effects of estrogen (ER) on OA. Objective: The study intended to conduct a meta-analysis of studies using ER in OA, aiming to confirm the potential value of ER, laying a foundation for follow-up research, and providing new choices for the treatment of OA. Design: The research team performed a literature review searching PubMed for clinical studies on the application of ER for the OA treatment or on the improvement of joint pain that: (1) were published after the year 2000, and (2) had participants who used ER compared to other treatment methods. The research team selected studies for analysis after independent screening by two members of the team, based on inclusion and exclusion criteria and a methodological quality evaluation. The meta-analysis used RevMan V5.3 software. Intervention: The research team included eight studies with 11 689 participants, with 5776 participants who received ER treatments becoming the intervention group, and with 5913 participants who received other treatments becoming the control group. Outcome Measures: The outcome measures included the selected studies' results related: (1) to changes in the bone marker, collagen cross-linked C-telopeptide type I (CTX-1); (2) to the levels of bone Gla protein (BGP); (3) to joint-pain relief, and (4) to subjective scores on the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), a visual analogue scale (VAS) for pain, and the Short-Form 36 (SF-36). Results: The meta-analysis found that the CTX-II level was significantly lower (P < .0001) and the BGP level was significantly higher (P = .07) in the EG group than the levels in the control group. Similarly, the number of participants with joint pain in the ER group was significantly lower than that of the control group (P = .01), and a significant difference existed between the groups in the subjective scores (P = .02). Conclusion: ER can exert varying degrees of positive effects on OA and can effectively ameliorate the pathological process in OA patients, and it may become an alternative for OA treatment in the future, providing patients with better health and life quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.048 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".