Intake of Phytoestrogen Foods and Supplements Among Women Recently Diagnosed With Breast Cancer in Ontario, Canada
Bibliographic record
Abstract
Phytoestrogens are found in foods such as soy (isoflavones) and flaxseed (lignans), and certain botanical supplements. Their role in estrogen receptor positive (ER+) breast cancer recurrence and treatment is controversial, and it is unknown how this affects intake among patients. The Ontario Cancer Registry was used to identify 417 population-based breast cancer cases (mean time from diagnosis was 57 days). A questionnaire was mailed to determine intake of phytoestrogen foods and supplements in the last 2 mo, changes since diagnosis and differences by ER tumor status or hormonal treatment. Of 278 (67%) respondents, 56% consumed soy foods, 39% consumed isoflavone-rich foods (tofu, soybeans, soy milk, soy nuts), and 70% ate lignan-rich foods, including flaxseed (33%). Only soy milk, flaxseed, and flaxseed bread were commonly consumed more than once/wk. Few patients (4%) took isoflavone (soy, red clover, kudzu, licorice, isoflavones) or lignan/flaxseed supplements. Since diagnosis, 17% started or stopped soy foods (most stopped); this was more prevalent among those receiving hormonal treatment (20%; 95% confidence interval (CI): 14, 26) than not (6%; 95% CI: 1, 12). No other differences by ER status or hormonal treatment were observed. Research is needed to confirm this and to explore influencing factors.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".