Does age at the start of treatment for vaginal atrophy predict response to vaginal estrogen therapy? Post hoc analysis of data from a randomized clinical trial involving 205 women treated with 10 μg estradiol vaginal tablets
Bibliographic record
Abstract
OBJECTIVE: Local estrogen therapy (ET) can improve vaginal atrophy symptoms and associated cellular changes in postmenopausal women. This study evaluated whether age at the start of treatment influences response. METHODS: This post hoc analysis used data from a double-blind, randomized, placebo-controlled trial (NCT00108849), which treated 205 postmenopausal women aged ≥45 years with 10 μg vaginal ET for 52 weeks.Women aged <60 or ≥60 years at treatment start were evaluated according to the following: vaginal maturation index (assessed by vaginal cytology samples), vaginal pH, and most bothersome symptom (both graded on four-point scales). Covariance analysis aimed to evaluate mean change differences between groups from baseline-week 52. RESULTS: Vaginal ET improved vaginal maturation index (for all cell layers), vaginal pH, and symptom scores for both age groups. However, cytological profiles were significantly different in the <60 (n = 143) versus ≥60 years group (n = 55, estimated effect: -3.7, P = 0.0003 [parabasal cells]; 5.8, P = 0.0002 [intermediate cells]), indicating reduced cellular responsiveness to treatment among older women. Treatment effect on vaginal pH was less for older women, with a between-group difference of -0.19 (standard error = 0.05; P = 0.0003). CONCLUSIONS: Findings suggest that treatment may be initiated at any age since low-dose vaginal ET improved symptoms and signs of vaginal atrophy in both younger (<60 years) and older (≥60 y) women. The stronger response observed in younger women supports current clinical recommendations to start treatment early. Continued treatment may be important to avoid recurrence of vaginal atrophy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".