Species‐level characterization of the cervicovaginal microbiota and its role in human papillomavirus‐associated cervical carcinogenesis
Bibliographic record
Abstract
The cervicovaginal microbiome may contribute to human papillomavirus (HPV)-associated cervical carcinogenesis, but studies have been limited by low-resolution analysis methods. Using a high-resolution bioinformatics pipeline, we evaluated the relationship of the cervicovaginal microbiome with HPV and cervical intraepithelial neoplasia (CIN). The cervicovaginal microbiome of 186 women was characterized by sequencing 16S rRNA regions (V3-V4 and V5-V6) and annotated with the high-resolution ANCHOR pipeline. Samples were genotyped for HPV using the Roche-Cobas 4800 assay. We fitted logistic regression models using stepwise forward selection to select species (presence/absence) as correlates of CIN1+ and constructed a linear microbiome-based score using the regression coefficients. An HPV-based score was calculated from a separate logistic regression model to detect CIN1+ . Receiver operating characteristic curve analyses were performed; the area under the curve (AUC) and 95% confidence intervals (CI) were compared between scores. Overall, 66.7% of participants were HPV-positive. 77 unique species were identified: 8 using V3-V4, 48 using V5-V6, and 21 shared. Twelve species were retained via stepwise selection. The AUCs for the microbiome-, and HPV-based scores were 0.7656 (95% CI 0.6885-0.8426), and 0.7529 (95% CI 0.6855-0.8204), respectively. Bacterial species may be involved in cervical carcinogenesis as the microbiome- and HPV-based scores performed similarly for CIN1+ detection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.001 |
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".