Alcohol Intake and Cigarette Smoking and Risk of a Contralateral Breast Cancer: The Women's Environmental Cancer and Radiation Epidemiology Study
Bibliographic record
Abstract
Women with primary breast cancer are at increased risk of developing second primary breast cancer. Few studies have evaluated risk factors for the development of asynchronous contralateral breast cancer in women with breast cancer. In the Women's Environmental Cancer and Radiation Epidemiology Study (1985-2001), the roles of alcohol and smoking were examined in 708 women with asynchronous contralateral breast cancer (cases) compared with 1,399 women with unilateral breast cancer (controls). Cases and controls aged less than 55 years at first breast cancer diagnosis were identified from 5 population-based cancer registries in the United States and Denmark. Controls were matched to cases on birth year, diagnosis year, registry region, and race and countermatched on radiation treatment. Risk factor information was collected by telephone interview. Rate ratios and 95% confidence intervals were estimated by using conditional logistic regression. Ever regular drinking was associated with an increased risk of asynchronous contralateral breast cancer (rate ratio = 1.3, 95% confidence interval: 1.0, 1.6), and the risk increased with increasing duration (P = 0.03). Smoking was not related to asynchronous contralateral breast cancer. In this, the largest study of asynchronous contralateral breast cancer to date, alcohol is a risk factor for the disease, as it is for a first primary breast cancer.
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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 | 0.002 |
| 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 | 0.001 |
| 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".