Can breast cancer patients use soyafoods to help reduce risk of CHD?
Bibliographic record
Abstract
Over the past 20 years, the popularity of soyafoods has increased in part because of research suggesting that these foods convey health benefits independent of their nutrient content. For example, in 1999, the US Food and Drug Administration approved a health-claim for soyafoods and CHD based on the hypocholesterolaemic effects of soya protein. However, soyafoods have become controversial in recent years because of concerns that their uniquely rich phyto-oestrogen (isoflavone) content may cause untoward effects in some individuals. Most notable in this regard is the concern that soyafoods are contraindicated for breast cancer patients and women at high risk of developing this disease. Furthermore, the hypocholesterolaemic effects of soya protein have been challenged. However, the results of recently published meta-analyses indicate that soya protein directly lowers circulating LDL-cholesterol levels by approximately 4 %. There is also intriguing evidence that soyafoods reduce CHD risk independent of their effects on lipid levels. In regard to the breast cancer controversy, recently published clinical and epidemiological data do not support observations in rodents that soyabean isoflavones increase breast cancer risk. In postmenopausal women, isoflavone exposure does not adversely affect breast tissue density or breast cell proliferation. Furthermore, both US and Chinese prospective epidemiological studies show that post-diagnosis soya consumption is associated with an improved prognosis. Therefore, soyafoods should be considered by women as healthy foods to include in diets aimed at reducing the risk of CHD regardless of their breast cancer status.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".