High Coffee Intake, but Not Caffeine, is Associated with Reduced Estrogen Receptor Negative and Postmenopausal Breast Cancer Risk with No Effect Modification by CYP1A2 Genotype
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Associations between caffeine and coffee consumption and breast cancer risk are uncertain, with studies suggesting inverse and null associations. Variation in cytochrome P450 1A2 (CYP1A2), a gene responsible for caffeine metabolism, may modify these associations. Cases (n = 3,062) were recruited through the Ontario Cancer Registry and controls (n = 3,427) through random digit dialing. Logistic regression was used to evaluate associations between breast cancer risk and intakes of 7 caffeine-containing items and total caffeine, and examine whether a genetic variant in CYP1A2 (rs762551) modified these associations. Analyses were stratified by estrogen receptor (ER), menopausal, and smoking status. Generally, coffee and caffeine were not associated with breast cancer risk; however, a significant reduction in risk was observed with the highest category of coffee consumption [≥5 cups per day vs. never, multivariate-adjusted odds ratio (MVOR) = 0.71, 95% confidence interval (CI): 0.51, 0.98]. Variant rs762551 did not modify associations. In stratified analyses, high coffee intake was associated with reduced risk of ER- (MVOR = 0.41, 95% CI: 0.19, 0.92) and postmenopausal breast cancer (MVOR = 0.63, 95% CI: 0.43, 0.94). High coffee consumption, but not total caffeine, may be associated with reduced risk of ER- and postmenopausal breast cancers, independent of CYP1A2 genotype. Further studies are needed to replicate these findings.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it