The Association between Alcohol Consumption and Breast Density: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Percent breast density (PBD) is a strong risk factor for breast cancer that is influenced by several other risk factors for the disease. Alcohol consumption is associated with an increased risk of breast cancer with an uncertain association with PBD. We have carried out a systematic review and meta-analysis to examine the association of alcohol consumption with PBD. METHODS: We searched nine databases to identify all relevant studies on the association between alcohol intake and breast density. Two independent investigators evaluated and selected 20 studies that were included in our analyses. We divided the studies into three groups according to the methods used to measure and analyze the association of breast density with alcohol consumption. RESULTS: Meta-analysis of the 11 studies that used quantitative methods to measure and analyze PBD as a continuous variable found a statistically significant difference in PBD when comparing the highest with the lowest alcohol level [β = 0.84; 95% confidence interval (CI), 0.12-1.56]. Three studies that used quantitative methods to measure PBD and categories of PBD for analysis had a summary OR = 1.81 (95% CI, 1.07-3.04). Five studies that used categories to classify PBD and analyze their association with alcohol intake had a summary OR = 1.78 (95% CI, 0.90-3.51). CONCLUSIONS: These results suggest that there is a positive association between alcohol intake and PBD. IMPACT: Alcohol may increase the risk of breast cancer associated with PBD. Cancer Epidemiol Biomarkers Prev; 26(2); 170-8. ©2016 AACR.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".