Energy Intake and Risk of Postmenopausal Breast Cancer: An Expanded Analysis in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) Cohort
Bibliographic record
Abstract
Although animal experiments have consistently shown a positive relationship between breast cancer and energy intake, evidence from human studies remains inconclusive. In the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial cohort, 29,170 women, ages 55 to 75 years, who successfully completed a food frequency questionnaire (FFQ) at entry (1993-2001), were followed through 2007, and 1,319 incident breast cancers were ascertained (median time from FFQ completion to diagnosis, 4.4 years). Women in the highest quartile of energy intake, relative to the lowest, had modestly, but significantly, increased breast cancer risk [multivariate relative risk (RR), 1.21; 95% confidence interval (95% CI), 1.03-1.42; P(trend) = 0.03]. The inclusion of body mass index and physical activity in the model reduced risk slightly (RR, 1.18; 95% CI, 1.00-1.39; P(trend) = 0.07). However, in similar analyses using energy intake from a FFQ administered approximately five years after entry (27,428 women; 806 incident breast cancers; median time from FFQ completion to diagnosis, 2.7 years), women in the highest and lowest quartiles of energy intake had similar risk. When follow-up time after the first FFQ was divided into three 4-year periods, the multivariate RRs for high versus low energy intake increased from 1.21 to 1.37 to 1.55 with increasing time since dietary assessment. Although the divergent results for the two FFQs could be due to subtle questionnaire differences, our findings suggest a modest positive association between energy intake and postmenopausal breast cancer that strengthens with time since dietary assessment.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".