Oral co‐infection with multiple alpha‐human papillomavirus and head and neck cancer risk
Bibliographic record
Abstract
In Canada, the incidence of human papillomavirus (HPV)-related head and neck cancer (HNC) is increasing. The role of multiple oral HPV infections in HNC etiology remains unclear, and evidence of HPV vaccination's effectiveness in reducing HNC incidence is limited. We investigated oral HPV co-infection patterns, estimated the association between multiple oral HPV infections and HNC risk, and the effect of eliminating vaccine-targeted HPV genotypes on HNC incidence. We used data from a case-control study with 460 incident HNC cases and 458 frequency-matched controls recruited from four Montreal hospitals. In-person interviews gathered life course exposure data, and exfoliated mouth and cancer site cells were analyzed for α-HPV genotypes using PCR. We assessed co-infecting α-HPV genotypes' independence using a Poisson model and estimated the association between multiple oral α-HPV infections and HNC risk using logistic regression. We also emulated a target trial, using targeted maximum likelihood estimation to evaluate the potential treatment effect of HPV vaccination on HNC. Among HPV-positive individuals (164 cases, 61 controls), 34.76% of cases and 31.15% of controls had multiple oral α-HPV infections. The observed distribution differed from expected under a mutually independent model of infection. Multiple α-HPV infections increased HNC risk [OR = 4.66; 95%CI: 2.59, 8.76]. In the entire population [average treatment effect = -0.007, 95%CI; -0.008, -0.005] and among individuals without vaccine-targeted HPV genotypes [average treatment effect on the treated = -0.04, 95%CI; -0.05, -0.03], HNC risk decreased. In conclusion, multiple oral α-HPV infections are common and increase HNC risk. Conversely, HPV vaccination holds promise in reducing HNC incidence.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".