Dietary isoflavone intake and all‐cause mortality in breast cancer survivors: The Breast Cancer Family Registry
Bibliographic record
Abstract
BACKGROUND Soy foods possess both antiestrogenic and estrogen‐like properties. It remains controversial whether women diagnosed with breast cancer should be advised to eat more or less soy foods, especially for those who receive hormone therapies as part of cancer treatment. METHODS The association of dietary intake of isoflavone, the major phytoestrogen in soy, with all‐cause mortality was examined in 6235 women with breast cancer enrolled in the Breast Cancer Family Registry. Dietary intake was assessed using a Food Frequency Questionnaire developed for the Hawaii‐Los Angeles Multiethnic Cohort among 5178 women who reported prediagnosis diet and 1664 women who reported postdiagnosis diet. Cox proportional‐hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS During a median follow‐up of 113 months (approximately 9.4 years), 1224 deaths were documented. A 21% decrease was observed in all‐cause mortality for women who had the highest versus lowest quartile of dietary isoflavone intake (≥1.5 vs < 0.3 mg daily: HR, 0.79; 95% confidence interval CI, 0.64‐0.97; Ptrend = .01). Lower mortality associated with higher intake was limited to women who had tumors that were negative for hormone receptors (HR, 0.49; 95% CI, 0.29‐0.83; Ptrend = .005) and those who did not receive hormone therapy for their breast cancer (HR, 0.68; 95% CI, 0.51‐0.91; Ptrend = .02). Interactions, however, did not reach statistical significance. CONCLUSIONS In this large, ethnically diverse cohort of women with breast cancer living in North America, a higher dietary intake of isoflavone was associated with reduced all‐cause mortality. Cancer 2017;123:2070–2079. © 2017 American Cancer Society.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 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".