Accumulating evidence on passive and active smoking and breast cancer risk
Bibliographic record
Abstract
The aim of the study was to examine the risk of breast cancer associated with passive and active smoking and to explore risk heterogeneity among studies. Nineteen of 20 located published studies of passive smoking and breast cancer risk among women met basic quality criteria. Pooled relative risk estimates for breast cancer were calculated for 1) life-long non-smokers with regular passive exposure to tobacco smoke and 2) women who smoked. They were compared to women categorized as never regularly exposed to tobacco smoke. The pooled risk estimate for breast cancer associated with passive smoking among life-long non-smokers was 1.27 (95% confidence interval (CI), 1.11-1.45). In the subset of 5 studies (all case-control studies) with more complete exposure assessment (quantitative long-term information on the 3 major sources of passive smoke exposure: childhood, adult residential and occupational), the pooled risk estimate for exposed non-smokers was 1.90 (95%CI, 1.53-2.37). For the 14 studies with less complete passive exposure measures the risk was 1.08 (95%CI, 0.99-1.19) overall, 1.16 for 7 case-control and 1.06 for 7 cohort studies, although dose-response results in 3 of 4 Asian cohort studies suggested increased risk. The overall premenopausal breast cancer risk associated with passive smoking among life-long non-smokers was 1.68 (95%CI 1.33-2.12), and 2.19 (95% CI 1.68-2.84) for the 5 of 14 studies with more complete exposure assessment. For women who had smoked the breast cancer risk estimate was 1.46 (95%CI 1.15-1.85) when compared to women with neither active nor regular passive smoke exposure; 2.08 (95% CI 1.44-3.01) for more complete and 1.15 (95% CI 0.92-1.43) for less complete passive exposure assessment. Studies with thorough passive smoking exposure assessment implicate passive and active smoking as risk factors for premenopausal breast cancer. Cohort studies with thorough passive smoking assessment would be helpful and studies exploring biological mechanisms are needed to explain the unexpected similarity of the passive and active risks.
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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 | 0.060 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".