Alcohol Use and Breast Cancer: A Critical Review
Bibliographic record
Abstract
The objective of this study was to outline the biological pathways of alcohol-attributable breast cancer, the epidemiological risk relationship between alcohol consumption and breast cancer, and the global burden of breast cancer incidence and mortality attributable to alcohol consumption, with a focus on light drinking. First, the literature regarding the biological mechanisms of how alcohol affects the risk of breast cancer was reviewed and summarized. Second, a search of meta-analyses that evaluated the risk relationship between alcohol consumption and breast cancer was conducted. Last, the burden of alcohol-attributable breast cancer incidence and mortality was estimated by means of a Population-Attributable Fraction methodology. Data on alcohol consumption were obtained from the Global Information System on Alcohol and Health, and data on cancer incidence and mortality were obtained from the GLOBOCAN database. Alcohol consumption affects breast cancer risk through the alteration in hormone levels and the associated biological pathways, the metabolism of ethanol resulting in carcinogens, and the inhibition of the one carbon metabolism pathway. The systematic review found 15 meta-analyses on the risk relationship between alcohol consumption (also light consumption) and the risk of breast cancer. All but 2 of these analyses showed a dose-response relationship between alcohol consumption and the risk of breast cancer. An estimated 144,000 (95% confidence interval [CI]: 88,000 to 200,000) breast cancer cases and 38,000 (95% CI: 2,400 to 53,000) breast cancer deaths globally in 2012 were attributable to alcohol, with 18.8% of these cases and 17.5% of these deaths affecting women who were light alcohol consumers. All levels of evidence showed a risk relationship between alcohol consumption and the risk of breast cancer, even at low levels of consumption. Due to this strong relationship, and to the amount of alcohol consumed globally, the incidence of and mortality from alcohol-attributable breast cancer is large.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".