Active Cigarette Smoking, Variants in Carcinogen Metabolism Genes and Breast Cancer Risk among Pre- and Postmenopausal Women in Ontario, Canada
Bibliographic record
Abstract
Cigarette smoking is strongly associated with various diseases including many cancers; however, evidence regarding breast cancer risk remains inconclusive with some studies reporting no association, and others an increased risk with long duration and early initiation of smoking. Genetic variation in carcinogen-metabolizing enzymes may modify these associations. Breast cancer cases were identified from the Ontario Cancer Registry (OCR) during 2003-2004 and population controls through random digit dialing methods. All subjects completed self-administered questionnaires. Subsequently, saliva samples were obtained from cases (N = 1,776) and controls (N = 1,839) for deoxyribonucleic acid (DNA) extraction. Multivariate logistic regression was used to estimate odds ratio (OR) and 95% confidence intervals (CI) for active smoking variables, and interactions were assessed between smoking and 36 carcinogen-metabolizing candidate gene variants. No statistically significant association was found between active smoking and breast cancer risk among all women nor when stratified by menopausal status; however, nonsignificant increased premenopausal breast cancer risk was observed among current smokers and women smoking before first pregnancy. Several statistically significant interactions were observed between smoking and genetic variants (CYP1A2 1548C>T, CYP1A1 3801T>C, CYP1B1 4326G>C, NAT1 c.-85-1014T>A, UGT1A7 W208R 622T>C, SOD2 c.47T>C, GSTT1 deletion). However, in analyses stratified by these genotypes, smoking ORs had wide confidence intervals (and with few exceptions included 1.0) making interpretations difficult. Active smoking was not associated with breast cancer risk, although several significant interactions were observed between smoking, carcinogen-metabolizing genetic variants, and breast cancer risk.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".