Prevalence of Alpha, Beta, and Gamma Human Papillomaviruses in Patients With Head and Neck Cancer and Noncancer Controls and Relation to Behavioral Factors
Bibliographic record
Abstract
BACKGROUND: Human papillomaviruses (HPVs) cause head and neck cancer (HNC), which is increasing in incidence in developed countries. We investigated the prevalence of alpha (α), beta (β), and gamma (γ) HPVs among HNC cases and controls, and their relationship with sociodemographic, behavioral, and oral health factors. METHODS: We obtained oral rinse and brush samples from incident HNC cases (n = 369) and hospital-based controls (n = 439) and tumor samples for a subsample of cases (n = 121). We genotyped samples using polymerase chain reaction with PGMY09-PGMY11 primers and linear array for α-HPV and type-specific multiplex genotyping assay for β-HPV and γ-HPV. Sociodemographic and behavioral data were obtained from interviews. RESULTS: The prevalence of α-, β-, and γ-HPV among controls was 14%, 56%, and 24%, respectively, whereas prevalence among cases was 42%, 50%, and 33%, respectively. Prevalence of α- and γ-HPV, but not β-HPV, increased with increase in sexual activity, smoking, and drinking habits. No HPV genus was associated with oral health. Tumor samples included HPV genotypes exclusively from the α-genus, mostly HPV-16, in 80% of cases. CONCLUSIONS: The distribution of α- and γ-HPV, but not β-HPV, seems to vary based on sociodemographic and behavioral characteristics. We did not observe the presence of cutaneous HPV in tumor tissues.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".