Prevalence and correlates of vaginal estrogenization in postmenopausal women in the United States
Bibliographic record
Abstract
OBJECTIVE: This work aims to establish current population-based vaginal estrogenization norms for postmenopausal US women. METHODS: Using a US national probability sample of 868 postmenopausal women ages 57 to 85 years (mean age 67.6 ± 0.3 y, 21.6 ± 0.5 y since menopause), we calculated the epithelial maturation value (MV) generated from self-collected vaginal specimens and compared findings with historical clinical data. Linear and logistic regressions were used to describe the relationship between vaginal estrogenization and sociodemographic, physical, gynecologic, and sexual characteristics. RESULTS: Among postmenopausal women, mean MV was 46.6 ± 0.8 (SD 17.4, range 2.5-100) and stable across age groups. In every age group, vaginal estrogenization was higher among postmenopausal nonusers of hormone therapy (HT) in the 2005-2006 US cohort than reported for the 1960s Canadian clinical cohort. MV was also higher among women who used postmenopausal HT in the prior 12 months compared with those who did not (55.1 ± 1.2 vs 44.4 ± 0.9, P < 0.001). In multivariate analyses, HT use, obesity and African American race were each independently associated with higher MV. Overall, MV was not associated with sexual activity, but low MV was associated with vaginal dryness during intercourse among sexually active women. CONCLUSIONS: Compared to 1960s clinical data, current population estimates revealed higher vaginal estrogenization across all age groups and no decline with age. The strongest independent correlates of vaginal estrogenization in postmenopausal US women were current HT use, obesity, and African American race. Postmenopause, half of all women exhibit low vaginal estrogenization.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".