Vaginal microbial diversity among postmenopausal women with and without hormone replacement therapy
Bibliographic record
Abstract
Urogenital infections in postmenopausal women remain problematic. The use of estrogen replacement therapy has been shown to lower these infection rates, corresponding to increasing colonization by Lactobacillus species. Despite the gut's 500 microbial species and the proximity of the anus to the vagina, only a relatively few microbial strains appear to be able to colonize the urogenital area. In the present study, the sparsity of microbes in the vagina was confirmed by denaturing gradient gel electrophoresis analysis of swabs taken at time zero and monthly for 3 months from 40 postmenopausal subjects receiving Premarin (conjugated equine estrogen in combination with progesterone) hormone replacement therapy (HRT) and 20 who were not on HRT. Lactobacilli were recovered from the vagina of 95% or more women in both groups, but in the HRT group, Lactobacillus were more often the dominant and only colonizers and significantly fewer bacteria with pathogenic potential were found. The incidence of bacterial vaginosis was significantly lower in the HRT group than in the non-HRT-treated women (5.6% versus 31%). The use of HRTs has recently come under criticism. The ability of drugs such as Premarin to help recover the lactobacilli vaginal microbiota appears to be at least one benefit of HRT use. In women not using HRTs, use of probiotics may be the only way to restore a nonpathogen-dominated flora.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".