A population-based, case-control study of breast cancer and alcohol consumption among postmenopausal women living in Montreal, Quebec, Canada /
Bibliographic record
Abstract
In the present population-based, case-control study of incident, postmenopausal breast cancer, we obtained an extensive history of alcohol consumption. Indices reflecting age-specific exposure, duration and cumulative exposure of alcohol were developed for specific types of alcoholic beverages as well as the combination of these beverages. Unconditional logistic regression, within the context of the Generalized Additive Models, was used to estimate adjusted odds ratios (OR) and 95% confidence intervals (CI). Case subjects included all new histologically-confirmed cases of malignant breast cancer among postmenopausal women, age 51--75 years, diagnosed or treated in 1996 and 1997 in all major hospitals in Montreal. Control subjects were selected randomly from other histologically-confirmed sites of cancer from the same hospitals as the cases. The response rate was 82% for cases and 75% for controls. Current drinkers of any kind of alcohol were at an increased risk of breast cancer (OR = 1.47; 95%CI: 1.01--2.15). In particular, the risk of breast cancer was increased by 1.6-fold among weekly and current exclusive drinkers of wine. Other factors suggestive of an increased risk of breast cancer include early-age at first consumption of alcohol (≤30 years old) and increased number of years (>15 years) of consuming wine among women who only drank wine. We did not find, however, monotonically increasing risks with levels of consumption. Although, the associations found were relatively weak, our findings provide further support for a positive association between the risk of breast cancer and alcohol consumption, particularly wine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".