Risk Factors Associated With Incidence of Lung Cancer in Never-Smokers: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objectives: Lung cancer is the leading cause of cancer mortality globally. Although often associated with smoking, up to 25% of cases worldwide and 50% in East Asia occur in "never-smokers." There are currently no robust tools for predicting lung cancer in individuals who have never smoked (LCINS) for populations outside East Asia.Together with a group of patient representatives, the authors of this study aimed to summarise risk factors for LCINS and quantify risk in different geographical regions. Methods: This study was prospectively registered (PROSPERO-CRD42022379253). The systematic review and meta-analysis included studies published from 2017 and aimed to comprehensively investigate risk factors associated with LCINS incidence. Risk of bias was assessed using Newcastle-Ottawa Scale. Results: A total of 6725 reports were identified and 54 studies were included, with multivariable analysis of 192 factors in 16 million never-smokers. No studies were assessed as having high risk of bias. Of the participants, 8,241,269 (51.0%) were from Western countries.The meta-analysis found that female sex (adjusted hazard ratio [aHR] 1.28 [95% confidence interval or CI 1.12-1.47]), previous cancer (aHR 2.04 [1.95-2.13]), rheumatoid arthritis (aHR 1.41 [1.15-1.73]), passive smoking (aHR 1.30 [1.22-1.40]), PM10 (aHR 1.10 [1.09-1.11]), and PM2.5 (aHR 1.16 [1.03-1.30]) pollution were associated with LCINS. In planned subgroup analyses by region, LCINS was associated with family history of lung cancer in East Asian (aHR 1.56 [1.23-1.98]) but not Western countries (aHR 0.86 [0.35-2.11]). Conclusion: We found key factors linked with LCINS, including female sex, rheumatoid arthritis, and pollution and, for the first time, quantified their association through meta-analyses of studies globally. This may be used to develop tools to detect LCINS earlier.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".