The association between smoking and cancer incidence in <i>BRCA1</i> and <i>BRCA2</i> mutation carriers
Bibliographic record
Abstract
Tobacco smoke is an established carcinogen, but the association between tobacco smoking and cancer risk in BRCA mutation carriers is not clear. The aim of this study was to evaluate prospectively the association between tobacco smoking and cancer incidence in a cohort of BRCA1 and BRCA2 mutation carriers. The study population consisted of unaffected BRCA mutation carriers. Information on lifestyle including smoking histories, reproductive factors, and past medical histories was obtained through questionnaires. Incident cancers were updated biennially via follow-up questionnaires. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using time-dependent Cox regression models. There were 700 incident cancers diagnosed over 26,711 person-years of follow-up. The most frequent cancers seen in BRCA mutation carriers were breast (n = 428; 61%) and ovarian (n = 109; 15%) cancer. Compared to nonsmokers, (ever) smoking was associated with a modest increased risk of all cancers combined (HR = 1.17; 95%CI 1.01-1.37). Women in the highest group of total pack-years (4.3-9.8) had an increased risk of developing any cancer (HR = 1.27; 95%CI 1.04-1.56), breast cancer (HR = 1.33, 95%CI 1.02-1.75), and ovarian cancer (HR = 1.68; 95%CI 1.06-2.67) compared to never smokers. The associations between tobacco smoking and cancer did not differ by BRCA mutation type or by age at diagnosis. This prospective study suggests that tobacco smoking is associated with a modest increase in the risks of breast and ovarian cancer among women with BRCA1 or BRCA2 mutation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".