Phytoestrogen Content of Foods Consumed in Canada, Including Isoflavones, Lignans, and Coumestan
Bibliographic record
Abstract
Phytoestrogens may play a role in hormone-related diseases such as cancer, but epidemiological and clinical data are conflicting in part due to inadequate databases used in intake estimation. A database of nine phytoestrogens in foods relevant to Western diets was developed to more accurately estimate intakes. Foods (N = 121) available in Ontario, Canada were prepared as commonly consumed and analyzed for isoflavones (genistein, daidzein, glycitein, formononetin), lignans (secoisolariciresinol, matairesinol, pinoresinol, lariciresinol), and coumestan (coumestrol) using gas chromatography-mass spectrometry methods. Data were presented on an as is (wet) basis per 100 g and per serving. Food groups with decreasing levels of total phytoestrogens per 100 g are nuts and oilseeds, soy products, cereals and breads, legumes, meat products, and other processed foods that may contain soy, vegetables, fruits, alcoholic, and nonalcoholic beverages. Soy products contain the highest amounts of isoflavone, followed by legumes, meat products and other processed foods, cereals and breads, nuts and oilseeds, vegetables, alcoholic beverages, fruits, and nonalcoholic beverages. Decreasing amounts of lignans are found in nuts and oilseeds, cereals and breads, legumes, fruits, vegetables, soy products, processed foods, alcoholic, and nonalcoholic beverages. The richest sources of specific phytoestrogens, including coumestrol, were identified. The database will improve phytoestrogen intake estimation in future epidemiological and clinical studies particularly in Western populations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".