Characteristics of menstruation and pregnancy and the risk of lung cancer in women
Bibliographic record
Abstract
Lung tissue, both normal and cancerous, has been found to express estrogen receptors and patterns of expression have differed between men and women, suggesting a possible role for hormone-related factors in lung carcinogenesis in women. Few epidemiological studies have examined hormone-related variables and lung cancer risk and the findings have not been consistent. We investigated the association between characteristics of menstruation and pregnancy in relation to lung cancer risk in a population-based case-control study carried out in Montreal, Canada, including 422 women with lung cancer and 577 controls. For each variable, odds ratios (OR) and 95% confidence intervals (CI) were estimated using unconditional logistic regression modeling. Associations were also examined according to level of smoking and by lung cancer histology. All statistical tests were two-sided. Most characteristics of menstruation and pregnancy were not associated with lung cancer risk. However, an increased risk was observed for women who had had a non-natural menopause, which predominantly included women who had had a bilateral oophorectomy, compared with women who had had a natural menopause (OR = 1.92, 95% CI: 1.22-3.01). An inverse association with age at menopause was suggested. These results did not vary by level of smoking and they were similar for adenocarcinomas compared with other histological types. Our results suggest that hormonal factors, related to early menopause and/or ovary removal, may play a role in the risk of lung cancer. Further studies are needed to confirm these findings, and to assess the possible contribution of hormone replacement therapy.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".