Associated risk and resilience factors of Alzheimer’s disease in women with early bilateral oophorectomy: Data from the UK Biobank
Bibliographic record
Abstract
Background: Bilateral oophorectomy (BO) confers immediate estradiol loss. We examined prevalence and predictors of Alzheimer's disease (AD) in women with early BO comparing their odds ratios of AD to those of women with spontaneous menopause (SM). Methods: A cohort from UK Biobank (n = 34,603) included women aged 60 + at baseline with and without AD who had early BO or SM. AD was determined based on AD related ICD-10 or ICD-9 code. We used logistic regression to model the association of menopause type with AD. Model predictors included age, education, age at menopause, hormone therapy (HT), APOE4, body mass index (BMI), cancer history, and smoking history. Results: Those with early BO had four times the odds of developing AD (OR = 4.12, 95% CI [2.02, 8.44]) compared to those with SM. APOE4 (OR = 4.29, 95% CI [2.43, 7.56]), and older age (OR = 1.16, 95% CI [1.05, 1.28]) were associated with increased odds of AD in the BO group. Greater years of education were associated with reduced odds of AD for both BO (OR = 0.91, 95% CI [0.85, 0.98]), and SM (OR = 0.95, 95% CI [0.90, 0.99]), while ever use of HT was associated with decreased odds of AD only for the BO group (OR = 0.43, 95% CI [0.23, 0.82]). Conclusions: Women with early BO, particularly with an APOE4 allele, are at high risk of AD. Women with early BO who use HT and those with increased education have lower odds of developing AD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".