Risk of Breast Cancer Among French-Canadian Women, Noncarriers of More Frequent<i>BRCA1/2</i>Mutations and Consumption of Total Energy, Coffee, and Alcohol
Bibliographic record
Abstract
Although the connection between diet, lifestyle and hormones suggests that nutritional and lifestyle factors may exert an influence in the etiology of breast cancer, it is not clear whether these factors operate in the same way in women without BRCA gene mutations. A nested case-control study was conducted in a cohort of French-Canadian women, with 560 members involving 280 nongene carriers of mutated BRCA gene affected by breast cancer and 280 nonaffected and nongene carriers of mutated BRCA gene. A validated semi-quantitative food frequency questionnaire was used to ascertain dietary intake, and a core questionnaire, to gather information on lifestyle risk factors. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated in logistic regression models. It was found that energy intake >2,057 Kcal/day was significantly and positively related to breast cancer risk (OR = 2.54; 95% CI: 1.67-3.84; p = 0.01). Women who drank more than eight cups of coffee per day had an increased risk of breast cancer: OR = 1.40 (95% CI: 1.09-2.24; p = 0.03). Subjects who drank >9 g of alcohol (ethanol) per day had an increased risk of breast cancer: OR = 1.55 (95% CI: 1.02-2.37; p = 0.04). In addition, a positive and significant association was noted between the consumption of beer, wine and spirits, and breast cancer risk. The ORs were 1.34 (95% CI: 1.28-2.11; p = 0.04) for more than two bottles of beer per week, OR = 1.16 (95% CI: 1.08-2.58; p = 0.05) for >10 oz of wine per week and OR = 1.09 (95% CI: 1.02-2.08; p = 0.05) for >6 oz of spirit per week, respectively. Intakes of other nutrients and dietary components were not significantly associated with nongene carrier breast cancer risk. This study provides evidence that total energy intake, coffee, and alcohol consumption may play a role in 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".