Alcohol consumption and cigarette smoking in combination: A predictor of contralateral breast cancer risk in the WECARE study
Bibliographic record
Abstract
Alcohol drinking and, to a lesser extent, cigarette smoking are risk factors for a first primary breast cancer. Information on these behaviours at diagnosis may contribute to risk prediction of contralateral breast cancer (CBC) and they are potentially modifiable. The WECARE Study is a large population-based case-control study of women with breast cancer where cases (N = 1,521) had asynchronous CBC and controls (N = 2,212), matched on survival time and other factors, had unilateral breast cancer (UBC). Using multivariable conditional logistic regression to estimate rate ratios (RR) and 95% confidence intervals (CI), we examined the risk of CBC in relation to drinking and smoking history at and following first diagnosis. We adjusted for treatment, disease characteristics and other factors. There was some evidence for an association between CBC risk and current drinking or current smoking at the time of first breast cancer diagnosis, but the increased risk occurred primarily among women exposed to both (RR = 1.62, 95% CI 1.24-2.11). CBC risk was also elevated in women who both smoked and drank alcohol after diagnosis (RR = 1.54, 95% CI 1.18-1.99). In the subset of women with detailed information on amount consumed, smoking an average of ≥10 cigarettes per day following diagnosis was also associated with increased CBC risk (RR = 1.50, 95% CI 1.08-2.08; p-trend = 0.03). Among women with a diagnosis of breast cancer, information on current drinking and smoking could contribute to the prediction of CBC risk. Women who both drink and smoke may represent a group who merit targeted lifestyle intervention to modify their risk of CBC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".