High-Ambient Air Pollution Exposure Among Never Smokers Versus Ever Smokers With Lung Cancer
Bibliographic record
Abstract
Introduction Air pollution may play an important role in the development of lung cancer in people who have never smoked, especially among East Asian women. The aim of this study was to compare cumulative ambient air pollution exposure between ever and never smokers with lung cancer. Methods A consecutive case series of never and ever smokers with newly diagnosed lung cancer were compared regarding their sex, race, and outdoor and household air pollution exposure. Using individual residential history, cumulative exposure to outdoor particulate matter (PM 2.5 ) in a period of 20 years was quantified with a high-spatial resolution global exposure model. Results Of the 1005 patients with lung cancer, 56% were females and 33% were never smokers. Compared with ever smokers with lung cancer, never smokers with lung cancer were significantly younger, more frequently Asian, less likely to have chronic obstructive pulmonary disease or a family history of lung cancer, and had higher exposure to outdoor PM 2.5 but lower exposure to secondhand smoke. Multivariable logistic regression analysis revealed a significant association with never-smoking patients with lung cancer and being female (OR = 4.01, 95% confidence interval [CI]: 2.76–5.82, p < 0.001), being Asian (OR Asian versus non-Asian = 6.48, 95% CI: 4.42–9.50, p < 0.001), and having greater exposure to air pollution (OR ln_PM2.5 = 1.79, 95% CI: 1.10–7.2.90, p = 0.019). Conclusions Compared with ever-smoking patients with lung cancer, never-smoking patients had strong associations with being female, being Asian, and having air pollution exposures. Our results suggest that incorporation of cumulative exposure to ambient air pollutants be considered when assessing lung cancer risk in combination with traditional risk factors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".