Influencing factors of lung cancer in nonsmoking women: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Epidemiological studies have shown that some factors other than smoking may affect the risk of lung cancer in women, but the results are controversial. We conducted a meta-analysis to summarize the influencing factors of lung cancer in nonsmoking women. METHODS: Both English and Chinese databases were searched for publications from 1990 to 2020. All included studies were assessed according to the Newcastle-Ottawa Scale (NOS). The pooled odds ratios (ORs) and 95% confidence interval (CI) of influential factors were analyzed using the meta-analysis method, and the publication bias and sensitivity were analyzed. RESULTS: Among the five categories, the pooled OR of cooking factors category was the highest. Among 42 influencing factors, there were frequent fried food (OR = 2.42, 95% CI: 1.73-3.38) and long menstrual cycle (0.54, 95% CI: 0.39-0.75). A positive association of history of lung diseases/family lung/all cancer with lung cancer among Asian nonsmoking women (1.82, 95% CI: 1.60-2.07). Unlike other regions, cooking factors were the main risk factor for lung cancer in Asian. CONCLUSION: The meta-analysis suggests that cooking habits, diet, passive smoking, history of cancer and lung disease, and female reproduction are related to lung cancer in nonsmoking women. However, additional studies are warranted to extend this finding.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".