Association of Oral Microbiome With Oral Human Papillomavirus Infection: A Population Study of the National Health and Nutrition Examination Survey, 2009–2012
Bibliographic record
Abstract
BACKGROUND: Oral human papillomavirus (HPV) infection and the oral microbiome are associated with oropharyngeal cancer. However, population-based data on the association of oral microbiome with oral HPV infection are limited. METHOD: A cross-sectional analysis of 5496 20-59-year-old participants in the 2009-2012 National Health and Nutrition Examination Survey was performed. Associations with oral HPV infection were assessed using multivariable logistic regression for oral microbiome α-diversity (within-sample diversity), and using principal coordinate analysis and permutational multivariate analysis of variance for β-diversity (between-sample heterogeneity). RESULTS: Overall, for α-diversity, a lower number of observed amplicon sequence variants (adjusted odds ratio [aOR] = 0.996; 95% confidence interval [CI] = .992-.999) and reduced Faith's phylogenetic diversity (aOR = 0.95; 95% CI = .90-.99) were associated with high-risk oral HPV infection. β-diversity showed differentiation of oral microbiome community by high-risk oral HPV infection as measured by Bray-Curtis dissimilarity (R2 = 0.054%; P = .029) and unweighted UniFrac distance (R2 = 0.046%; P = .045). There were differential associations when stratified by sex. CONCLUSIONS: Both oral microbiome α-diversity and β-diversity were marginally associated with oral HPV infection. Longitudinal studies are needed to characterize the role of the microbiome in the natural history of oral HPV infection.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".