Passive Cigarette Smoke Exposure During Various Periods of Life, Genetic Variants, and Breast Cancer Risk Among Never Smokers
Bibliographic record
Abstract
The association between passive cigarette smoke exposure and breast cancer risk is inconclusive and may be modified by genotype. The authors investigated lifetime passive cigarette smoke exposures, 36 variants in 12 carcinogen-metabolizing genes, and breast cancer risk among Ontario, Canada, women who had never smoked (2003-2004). DNA (saliva) was available for 920 breast cancer cases and 960 controls. Detailed information about passive smoke exposure was collected for multiple age periods (childhood, teenage years, and adulthood) and environments (home, work, and social). Adjusted odds ratios and 95% confidence intervals were estimated by multivariable logistic regression, and statistical interactions were assessed using the likelihood ratio test. Among postmenopausal women, most associations between passive smoke and breast cancer risk were null, whereas among premenopausal women, nonsignificant positive associations were observed. Significant interactions were observed between certain types of passive smoke exposure and genetic variants in CYP2E1, NAT2, and UGT1A7. While these interactions were statistically significant, the magnitudes of the effect estimates were not consistent or easily interpretable, suggesting that they were perhaps due to chance. Although the results of this study were largely null, it is possible that premenopausal women exposed to passive smoke or carrying certain genetic variants may be at higher risk of breast cancer.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".