Smoking and alcohol by HPV status in head and neck cancer: a Mendelian randomization study
Bibliographic record
Abstract
HPV-positive and HPV-negative head and neck squamous cell carcinoma (HNSCC) are recognized as distinct entities. There remains uncertainty surrounding the causal effects of smoking and alcohol on the development of these two cancer types. Here we perform multivariable Mendelian randomization (MR) to evaluate the causal effects of smoking and alcohol on the risk of HPV-positive and HPV-negative HNSCC in 3431 cases and 3469 controls. Lifetime smoking exposure, as measured by the Comprehensive Smoking Index (CSI), is associated with increased risk of both HPV-negative HNSCC (OR = 3.03, 95%CI:1.75-5.24, P = 7.00E-05) and HPV-positive HNSCC (OR = 2.73, 95%CI:1.39-5.36, P = 0.003). Drinks Per Week is also linked with increased risk of both HPV-negative HNSCC (OR = 7.72, 95%CI:3.63-16.4, P = 1.00E-07) and HPV-positive HNSCC (OR = 2.66, 95%CI:1.06-6.68, P = 0.038). Smoking and alcohol independently increase the risk of both HPV-positive and HPV-negative HNSCC. These findings have important implications for understanding the modifying risk factors between HNSCC subtypes. Head and neck squamous cell carcinoma can occur with and without the presence of HPV infection. Here, the authors utilise Mendelian randomization to assess the causal effects of smoking and alcohol on HPV-positive and HPV-negative head and neck cancer development.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".