Vaginal Microbiome Components as Correlates of Cervical Human Papillomavirus Infection
Bibliographic record
Abstract
BACKGROUND: Interplay between vaginal microbiome and human papillomavirus (HPV) remains unclear, partly due to heterogeneity of microbiota. METHODS: We used data from 546 women enrolled in a cross-sectional study in 5 Brazil. We genotyped vaginal samples for HPV and sequenced V3-V4 region of 16S rRNA gene for vaginal microbiome analysis. We used stepwise logistic regression to construct 2 linear scores to predict high-risk HPV (hrHPV) positivity: one based exclusively on presence of individual bacterial taxa (microbiome-based [MB] score) and the other exclusively on participants' sociodemographic, behavioral, and clinical (SBC) characteristics. MB score combined coefficients of 30 (of 116) species. SBC score retained 6 of 25 candidate variables. We constructed receiver operating characteristic curves for scores as hrHPV correlates and compared areas under the curve (AUC) and 95% confidence intervals (CI). RESULTS: Overall, prevalence of hrHPV was 15.8%, and 26.2% had a Lactobacillus-depleted microbiome. AUCs were 0.8022 (95% CI, .7517-.8527) for MB score and 0.7027 (95% CI, .6419-.7636) for SBC score (P = .0163). CONCLUSIONS: The proposed MB score is strongly correlated with hrHPV positivity-exceeding the predictive value of behavioral variables-suggesting its potential as an indicator of infection and possible value for clinical risk stratification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".