Alcohol consumption and breast cancer risk subtypes in the E3N-EPIC cohort
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
The aim of this study was to obtain an overview of the associations between alcohol consumption and breast cancer risk at adulthood, by type of alcohol and subtype of breast cancer. Between 1993 and 2008, 66,481 women from the French E3N-EPIC cohort were followed up and asked to report their alcohol consumption, by type of alcohol, through a 208-item diet-history questionnaire. A total of 2812 breast cancer cases were validated during the follow-up session. No association was found between high alcohol consumption, whatever its type, and increase in breast cancer risk in the premenopausal period. During the postmenopausal period, a linear association between total alcohol consumption and breast cancer risk was found (P<0.0001), mainly driven by the associations with wine and beer [hazard ratio=1.33 (1.11-1.58) and 1.85 (1.19-2.89)] for more than two glasses per day of wine and beer, respectively, compared with nondrinkers] and with ER+/PR+ breast cancer subtypes. In the postmenopausal period, we observed interactions between total alcohol and folate intake levels (P=0.1192) and BMI (P=0.0367), with higher increased risks observed for high alcohol intake among women with low folate intake or who were overweight or obese. Our results make precise the current body of knowledge on the relationship between alcohol and breast cancer subtypes. Interactions between alcohol and other factors should further be taken into account in public health nutrition programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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) | 0.000 | 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