Menopausal state and rheumatoid arthritis: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Rheumatoid arthritis (RA) is a chronic inflammatory condition primarily affecting the joints. The higher prevalence of RA among females, combined with the known effects of sex hormones on immune function, has led researchers to investigate the potential relationship between menopausal status and the risk, severity, or progression of RA. This systematic review and meta-analysis aimed to determine the association between menopause and rheumatoid arthritis. METHODS: In 2023, we conducted a comprehensive search across multiple databases, including Google Scholar, Scopus, PubMed/MEDLINE, Science Direct, Web of Science, EMBASE, Springer, and ProQuest. The search aimed to identify studies exploring the association between menopause and rheumatoid arthritis. RESULTS: Our analysis revealed that post-menopausal women had a higher risk of developing rheumatoid arthritis compared to pre-menopausal women, with an estimated odds ratio of 1.35 (95% CI: 1.04-1.67). Additionally, women who experienced early menopause (defined as onset before age 45) showed significantly higher odds of developing RA, with an odds ratio of 2.97 (95% CI: 1.73-4.22). CONCLUSION: These findings highlight the importance of considering menopausal status when assessing the risk of RA development in women. The results suggest that post-menopausal women, particularly those who experience early menopause, may be at higher risk for developing RA. Further research in this area could provide valuable insights into potential preventive measures and targeted interventions for high-risk individuals.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".