Vaginal microbiome in women from Greenland assessed by microscopy and quantitative PCR
Bibliographic record
Abstract
BACKGROUND: Bacterial vaginosis (BV) is a common condition, although its aetiology remains unexplained. The aim of this study was to analyse the composition of vaginal microbiota in women from Greenland to provide a quantitative description and improve the understanding of BV. METHODS: Self-collected vaginal smears and swabs were obtained from 177 women. The vaginal smears were graded for BV according to Nugent's criteria. The vaginal swab samples were analysed by 19 quantitative PCRs (qPCRs) for selected vaginal bacteria and by PCR for four sexually transmitted infections (STIs). RESULTS: STIs were common: Mycoplasma genitalium 12%, Chlamydia trachomatis 7%, Neisseria gonorrhoeae 1%, and Trichomonas vaginalis 0.5%. BV was found in 45% of women, but was not associated with individual STIs. Seven of the 19 vaginal bacteria (Atopobium vaginae, Prevotella spp., Gardnerella vaginalis, BVAB2, Eggerthella-like bacterium, Leptotrichia amnionii, and Megasphaera type 1) had areas under the receiver operating characteristic (ROC) curve > 85%, suggesting they are good predictors of BV according to Nugent. Prevotella spp. had the highest odds ratio for BV (OR 437; 95% CI 82-2779) in univariate analysis considering only specimens with a bacterial load above the threshold determined by ROC curve analysis as positive, as well as the highest adjusted odds ratio in multivariate logistic regression analysis (OR 4.4; 95% CI 1.4-13.5). BV could be subdivided into clusters dominated by a single or a few species together. CONCLUSIONS: BV by Nugent score was highly prevalent. Two of seven key species (Prevotella spp. and A. vaginae) remained significantly associated with BV in a multivariate model after adjusting for other bacterial species. G. vaginalis and Prevotella spp. defined the majority of BV clusters.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".