Association of parity with the timing and type of menopause: a longitudinal cohort study
Bibliographic record
Abstract
We investigated the time-varying association between parity and timing of natural menopause, surgical menopause, and premenopausal hysterectomy among 23 728 women aged 40-65 years at enrollment in the Alberta's Tomorrow Project cohort study (2000-2022), using flexible parametric survival analysis. Overall, natural menopause was most common by study end (57.2%), followed by premenopausal hysterectomy (11.4%) and surgical menopause (5.3%). Risks of natural menopause before age 50 years were elevated for 0 births (adjusted hazard ratio [aHR] at age 45, 1.33; 95% CI, 1.18-1.49) and 1 birth (aHR age 45, 1.21; 95% CI, 1.07-1.38), but similar for ≥3 births (aHR age 45, 0.95; 95% CI, 0.85-1.06) compared to 2 births (reference). Elevated risks of surgical menopause before age 45 years for 0 births (aHR age 40, 1.37; 95% CI, 1.09-1.69) and 1 birth (aHR age 40, 1.11; 95% CI, 0.85-1.45) attenuated when excluding women with past infertility or recurrent pregnancy loss, and reduced risks were observed over time for ≥3 births (aHR age 50, 0.84; 95% CI, 0.75-0.94). Risks of premenopausal hysterectomy were lower before age 50 years for 0 births (aHR age 45, 0.82; 95% CI, 0.76-0.88) but elevated after age 40 years for ≥3 births (aHR age 50, 1.25; 95% CI, 1.08-1.45). These complex associations necessitate additional research on the sociobiological impacts of childbearing on gynecologic health.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".