Tobacco Smoking and Risk of Second Primary Lung Cancer
Bibliographic record
Abstract
Introduction Lung cancer survivors are at high risk of developing a second primary lung cancer (SPLC). However, SPLC risk factors have not been established and the impact of tobacco smoking remains controversial. We examined the risk factors for SPLC across multiple epidemiologic cohorts and evaluated the impact of smoking cessation on reducing SPLC risk. Methods We analyzed data from 7059 participants in the Multiethnic Cohort (MEC) diagnosed with an initial primary lung cancer (IPLC) between 1993 and 2017. Cause-specific proportional hazards models estimated SPLC risk. We conducted validation studies using the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (N = 3423 IPLC cases) and European Prospective Investigation into Cancer and Nutrition (N = 4731 IPLC cases) cohorts and pooled the SPLC risk estimates using random effects meta-analysis. Results Overall, 163 MEC cases (2.3%) developed SPLC. Smoking pack-years (hazard ratio [HR] = 1.18 per 10 pack-years, p < 0.001) and smoking intensity (HR = 1.30 per 10 cigarettes per day, p < 0.001) were significantly associated with increased SPLC risk. Individuals who met the 2013 U.S. Preventive Services Task Force's screening criteria at IPLC diagnosis also had an increased SPLC risk (HR = 1.92; p < 0.001). Validation studies with the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial and European Prospective Investigation into Cancer and Nutrition revealed consistent results. Meta-analysis yielded pooled HRs of 1.16 per 10 pack-years ( p meta < 0.001), 1.25 per 10 cigarettes per day ( p meta < 0.001), and 1.99 ( p meta < 0.001) for meeting the U.S. Preventive Services Task Force's criteria. In MEC, smoking cessation after IPLC diagnosis was associated with an 83% reduction in SPLC risk (HR = 0.17; p < 0.001). Conclusions Tobacco smoking is a risk factor for SPLC. Smoking cessation may reduce the risk of SPLC. Additional strategies for SPLC surveillance and screening are warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".