Bacterial Vaginosis Diagnosed by Analysis of First-Void-Urine Specimens
Bibliographic record
Abstract
Bacterial vaginosis (BV) is traditionally diagnosed using vaginal samples. The aim of this study was to investigate whether BV can be diagnosed from first-void urine (FVU). Self-collected vaginal smears, vaginal swabs, and FVU were obtained from 176 women. BV was diagnosed by Nugent's criteria. The FVU and vaginal swabs were analyzed by quantitative PCRs (qPCRs) for selected vaginal bacteria (Atopobium vaginae, Prevotella spp., Gardnerella vaginalis, bacterial vaginosis-associated bacterium 2, Eggerthella-like bacterium, "Leptotrichia amnionii," Megasphaera type 1), and all had an area under the receiver operating characteristic (ROC) curve of >85%, suggesting good prediction of BV according to the Nugent score. All seven bacteria in FVU were significantly associated with BV in univariate analysis. An accurate diagnosis of BV from urine was obtained in this population by a combination of qPCRs for Megasphaera type 1 and Prevotella spp. The same two bacteria remained significantly associated with BV in a multivariate model after adjusting for the other five species. There was no statistically significant difference between the sensitivities and specificities of BV diagnosis by molecular methods performed on swabs and FVU samples. A linear regression analysis showed good agreement between bacterial loads from swabs and FVU, but Prevotella spp. could be detected in high numbers in a few FVU samples without being present in swabs. This method will allow diagnosis of BV in studies where only urine has been collected and where detection of BV is considered relevant.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".