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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".