Alcohol consumption and breast cancer risk by estrogen receptor status: in a pooled analysis of 20 studies
Bibliographic record
Abstract
BACKGROUND: Breast cancer aetiology may differ by estrogen receptor (ER) status. Associations of alcohol and folate intakes with risk of breast cancer defined by ER status were examined in pooled analyses of the primary data from 20 cohorts. METHODS: During a maximum of 6-18 years of follow-up of 1 089 273 women, 21 624 ER+ and 5113 ER- breast cancers were identified. Study-specific multivariable relative risks (RRs) were calculated using Cox proportional hazards regression models and then combined using a random-effects model. RESULTS: Alcohol consumption was positively associated with risk of ER+ and ER- breast cancer. The pooled multivariable RRs (95% confidence intervals) comparing ≥ 30 g/d with 0 g/day of alcohol consumption were 1.35 (1.23-1.48) for ER+ and 1.28 (1.10-1.49) for ER- breast cancer (Ptrend ≤ 0.001; Pcommon-effects by ER status: 0.57). Associations were similar for alcohol intake from beer, wine and liquor. The associations with alcohol intake did not vary significantly by total (from foods and supplements) folate intake (Pinteraction ≥ 0.26). Dietary (from foods only) and total folate intakes were not associated with risk of overall, ER+ and ER- breast cancer; pooled multivariable RRs ranged from 0.98 to 1.02 comparing extreme quintiles. Following-up US studies through only the period before mandatory folic acid fortification did not change the results. The alcohol and folate associations did not vary by tumour subtypes defined by progesterone receptor status. CONCLUSIONS: Alcohol consumption was positively associated with risk of both ER+ and ER- breast cancer, even among women with high folate intake. Folate intake was not associated with 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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.026 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".