Does nicotine replacement therapy cause cancer? Evidence from the Lung Health Study
Bibliographic record
Abstract
INTRODUCTION: Recent genetic evidence has implicated nicotine as a possible cause of cancer, suggesting the need to examine the potential contributions of nicotine itself to cancer versus the confounding effects of addiction and thus exposures to known carcinogens. The objective of this study was to examine the relationship between nicotine replacement therapy, smoking, and cancer outcomes. METHODS: The Lung Health Study enrolled 5,887 participants in a randomized trial to prevent chronic obstructive pulmonary disease. The present study used surveillance data on 3,320 intervention participants who enrolled in the Lung Health Study for 5 years and who were then followed by the Lung Cancer Substudy for 7.5 years. Nicotine replacement therapy use and smoking exposure were recorded during the 5-year Lung Health Study trial. Surveillance for lung cancer, gastrointestinal cancer (including oral cancers), and all cancers began following the Lung Health Study. RESULTS: Adjusted Cox proportional hazards regressions assessed the hazards of nicotine replacement therapy and smoking for each diagnosis group. In the adjusted models for lung cancer, nicotine replacement therapy alone was not a significant predictor (p = .57), while smoking during the Lung Health Study was a significant predictor (p = .03). When nicotine replacement therapy and smoking were entered in the same model, nicotine replacement therapy remained not significant (p = .25) and smoking was clearly significant (p = .02). Nicotine replacement therapy and smoking were not significant predictors of cancer in the models for gastrointestinal cancer or all cancers. DISCUSSION: Although the surveillance time was short, smoking predicted cancer in this analysis and nicotine replacement therapy did not.
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".