Unilateral Oophorectomy and Age at Natural Menopause: A Longitudinal Community‐Based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To determine the association between unilateral oophorectomy (UO) and age at natural menopause. DESIGN: Secondary analysis of survey data from Alberta's Tomorrow Project (2000-2022). SETTING: Prospective cohort study in Alberta, Canada. POPULATION: 23 630 women; 548 experienced UO and 23 082 did not experience UO. METHODS: Flexible parametric survival analysis was used to analyse age at natural menopause, and logistic regression was used to analyse early menopause and premature ovarian insufficiency by UO status, controlling for birth year, parity, age at menarche, past infertility, hormonal contraceptive use and smoking. MAIN OUTCOME MEASURES: Age at natural menopause occurred by a final menstrual period without medical cause and sub-classified as early menopause (< 45 years) and premature ovarian insufficiency (< 40 years). RESULTS: Compared to no UO, any UO was associated with elevated risk of earlier age at natural menopause, which was strongest in early midlife (adjusted HR at age 40 1.71, 95% CI 1.31-2.19) and diminished over time. Compared to age 55 years at UO, risks of earlier age at natural menopause were largest and uniform in magnitude when UO occurred between approximately ages 20-40 years (adjusted HR for UO at age 30 2.32, 1.46-3.54) and then diminished as age at UO approached the average age at natural menopause. Any UO was associated with higher odds of early menopause (adjusted OR 1.90, 1.30-2.79) and premature ovarian insufficiency (adjusted OR 3.75, 1.72-8.16). CONCLUSIONS: Unilateral oophorectomy is associated with earlier age at natural menopause, particularly when performed before 40 years of age.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".