Menstrual and reproductive factors and lung cancer risk: A pooled analysis from the international lung cancer consortium
Bibliographic record
Abstract
Many clinical features of lung cancer are different in women and men. Sex steroid hormones exert effects in nonreproductive organs, such as the lungs. The association between menstrual and childbearing factors and the risk of lung cancer among women is still debated. We performed a pooled analysis of eight studies contributing to the International Lung Cancer Consortium (4,386 cases and 4,177 controls). Pooled associations between menstrual or reproductive factors and lung cancer were estimated using multivariable unconditional logistic regression. Subgroup analyses were done for menopause status, smoking habits and histology. We found no strong support for an association of age at menarche and at menopause with lung cancer, but peri/postmenopausal women were at higher risk compared to premenopausal (OR 1.47, 95% CI 1.11-1.93). Premenopausal women showed increased risks associated with parity (OR 1.74, 95% CI 1.03-2.93) and number of children (OR 2.88, 95% CI 1.21-6.93 for more than 3 children; p for trend 0.01) and decreased with breastfeeding (OR 0.54, 95% CI 0.30-0.98). In contrast, peri/postmenopausal subjects had ORs around unity for the same exposures. No major effect modification was exerted by smoking status or cancer histology. Menstrual and reproductive factors may play a role in the genesis of lung cancer, yet the mechanisms are unclear, and smoking remains the most important modifiable risk factor. More investigations in large well-designed studies are needed to confirm these findings and to clarify the underlying mechanisms of gender differences in lung cancer risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.030 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".