Exposure to secondhand tobacco smoke and lung cancer by histological type: A pooled analysis of the International Lung Cancer Consortium (ILCCO)
Bibliographic record
Abstract
While the association between exposure to secondhand smoke and lung cancer risk is well established, few studies with sufficient power have examined the association by histological type. In this study, we evaluated the secondhand smoke-lung cancer relationship by histological type based on pooled data from 18 case-control studies in the International Lung Cancer Consortium (ILCCO), including 2,504 cases and 7,276 control who were never smokers and 10,184 cases and 7,176 controls who were ever smokers. We used multivariable logistic regression, adjusting for age, sex, race/ethnicity, smoking status, pack-years of smoking, and study. Among never smokers, the odds ratios (OR) comparing those ever exposed to secondhand smoke with those never exposed were 1.31 (95% CI: 1.17-1.45) for all histological types combined, 1.26 (95% CI: 1.10-1.44) for adenocarcinoma, 1.41 (95% CI: 0.99-1.99) for squamous cell carcinoma, 1.48 (95% CI: 0.89-2.45) for large cell lung cancer, and 3.09 (95% CI: 1.62-5.89) for small cell lung cancer. The estimated association with secondhand smoke exposure was greater for small cell lung cancer than for nonsmall cell lung cancers (OR=2.11, 95% CI: 1.11-4.04). This analysis is the largest to date investigating the relation between exposure to secondhand smoke and lung cancer. Our study provides more precise estimates of the impact of secondhand smoke on the major histological types of lung cancer, indicates the association with secondhand smoke is stronger for small cell lung cancer than for the other histological types, and suggests the importance of intervention against exposure to secondhand smoke in lung cancer prevention.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.014 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".