Diet Quality and All-Cause Mortality in Women with Breast Cancer from the Breast Cancer Family Registry
Bibliographic record
Abstract
BACKGROUND: The impact of diet on breast cancer survival remains inconclusive. We assessed associations of all-cause mortality with adherence to the four diet quality indices: Healthy Eating Index-2015 (HEI-2015), Alternative Healthy Eating Index (AHEI), Alternative Mediterranean Diet (aMED), and Dietary Approaches to Stop Hypertension (DASH). METHODS: Dietary intake data were evaluated for 6,157 North American women enrolled in the Breast Cancer Family Registry who had been diagnosed with invasive breast cancer from 1993 to 2011 and were followed through 2018. Pre-diagnosis (n = 4,557) or post-diagnosis (n = 1,600) dietary intake was estimated through a food frequency questionnaire. During a median follow-up time of 11.3 years, 1,265 deaths occurred. Cox proportional hazards models were used to estimate multivariable-adjusted HR and 95% confidence intervals (CI). RESULTS: Women in the highest versus lowest quartile of adherence to the HEI-2015, AHEI, aMED, and DASH indices had a lower risk of all-cause mortality. HR (95% CI) were 0.88 (0.74-1.04; Ptrend = 0.12) for HEI-2015; 0.82 (0.69-0.97; Ptrend = 0.02) for AHEI; 0.73 (0.59-0.92; Ptrend = 0.02) for aMED; and 0.78 (0.65-0.94; Ptrend = 0.006) for DASH. In subgroup analyses, the associations with higher adherence to the four indices were similar for pre- or post-diagnosis dietary intake and were confined to women with a body mass index <25 kg/m2 and women with hormone receptor positive tumors. CONCLUSIONS: Higher adherence to the HEI-2015, AHEI, aMED, and DASH indices was associated with lower mortality among women with breast cancer. IMPACT: Adherence to a healthy diet may improve survival of women with breast cancer.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".