A cohort study on the associations between age at natural menopause and rheumatoid arthritis in postmenopausal women from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Menopause represents a significant phase in a woman's life, marked by profound physiological changes. An early onset of menopause has been associated with a variety of negative outcomes. Estrogen has been shown to be protective of bone and joint health. Hormonal links to rheumatoid arthritis have been found; previous studies exploring age at natural menopause (ANM) and Rheumatoid arthritis have produced conflicting results. This study investigated the association between ANM and incidence of rheumatoid arthritis among postmenopausal Canadian women. The study included women between the ages of 45-85 years from the Canadian Longitudinal Study on Aging followed over a 10-year period. Analysis was restricted to naturally postmenopausal women that did not have rheumatoid arthritis prior to menopause. ANM was examined using the following categories ≤ 44 (reference), 45-49, and ≥50. Survival analysis was used to determine time to onset of rheumatoid arthritis. Unadjusted and adjusted multivariable Cox regression models were used to examine the relationship between ANM and incidence of rheumatoid arthritis. The adjusted multivariable Cox regression model showed significantly lower risk of rheumatoid arthritis in women with an older ANM of ≥50 years and who have been on hormone replacement therapy for ≥8 years with a hazard ratio of 0.2 (95 % CI: 0.1-0.7) compared to women with an ANM ≤ 44 who have never used hormone replacement therapy. Our findings suggest a potential beneficial effect of longer estrogen exposure on the risk of developing rheumatoid arthritis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".