Evaluation of the Bacterial Vaginal Flora of 20 Postmenopausal Women by Direct (Nugent Score) and Molecular (Polymerase Chain Reaction and Denaturing Gradient Gel Electrophoresis) Techniques
Bibliographic record
Abstract
To monitor the bacterial vaginal microflora of postmenopausal women, several approaches were used: (1) Nugent scoring of Gram-stained smears from vaginal swabs; (2) testing for bacteria, using specific polymerase chain reaction (PCR) primers; (3) PCR-denaturing gradient gel electrophoresis (DGGE) analysis of total bacterial DNA, amplified using eubacterial and Lactobacillus-specific primers; and (4) sequence analysis of amplified DNA fragments of interest from denaturing gradient gels. Of 20 women studied, 70% had either intermediate-grade bacterial colonization or bacterial vaginosis (BV), as defined by Nugent criteria, at the first sampling point. Subjects with normal Nugent scores had >/=1 species of Lactobacillus detected by sequencing of dominant DNA fragments from denaturing gradient gels generated using eubacterial primers. Samples from women with Nugent scores that indicated BV had varied bacterial DGGE profiles, which emphasizes the diverse nature of such infections. This study indicates that asymptomatic BV appears to be much more common than is currently perceived; these findings may have implications for the health of postmenopausal women.
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 | 0.002 |
| 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".