Latency of tobacco smoking for head and neck cancer among HPV‐positive and HPV‐negative individuals
Bibliographic record
Abstract
Human papillomavirus (HPV) infection and tobacco smoking are well‐known risk factors for head and neck cancers (HNC). Although an effect modification between oral HPV infection and tobacco smoking may exist, evidence is lacking on how they interact temporally. We investigated the latency and life course effects of tobacco smoking on risk of HNC among HPV‐positive (HPV+ve) and negative (HPV‐ve) individuals. We used data from 631 ever‐smoker participants of a hospital‐based case–control study conducted in four major hospitals in Montréal, Canada. Cases (n = 320), incident, histologically confirmed, primary squamous cell carcinomas, were frequency‐matched to controls (n = 311) by age and sex. Sociodemographic and behavioral factors (e.g., tobacco and alcohol use and sexual history) were collected using a structured interview applying a life grid technique. Oral exfoliated cells were used for HPV DNA detection and genotyping. Latency effects were estimated flexibly using a Bayesian relevant exposure model and further extended with a life course approach. Retrospective smoking trajectories for HPV+ve cases and controls had similar shapes. Exposure to tobacco smoking even 40 years before diagnosis was associated with an increased HNC risk among both HPV+ve and HPV‐ve participants. The effect of smoking before the start of sexual activity compared to afterwards was higher among HPV+ve individuals. This pattern of association was less profound among HPV‐ve participants. Temporal interactions may exists between oral HPV infection and life course smoking trajectories in relation to HNC risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".