May isoflavones prevent breast cancer risk?
Bibliographic record
Abstract
Breast cancer still represents a challenge in Brazil, as it is the most frequent malignant neoplasm with more than 14,000 deaths recorded in 2014.It is the leading cause of death in the female population 1 .It is also commonly diagnosed among women in Western countries as the second leading cause of cancer 2 .It is the sixth leading cause of mortality among women, after acute myocardial infarction, pneumonia, diabetes, stroke, and chronic obstructive pulmonary disease.Breast cancer is a heterogeneous disease and can be classified by clinical, histopathological, and molecular parameters 3 .In this classification, the estrogen receptors play an important role and seem to influence the development of breast neoplasms or response to treatment 4 .However, women with breast cancer face the consequences of hypoestrogenism due to chemotherapy treatment or being postmenopausal.Thus, several substances are being suggested to reduce the vasomotor symptoms that appear in these women.The effects of soy isoflavones, due to their structural similarity and molecular size that resemble estrogens, have the ability to bind with greater affinity to estrogen beta-receptors; for this reason, they are called phytoestrogens [5][6][7] .The binding of phytoestrogen to the receptor can result in partial activation of the receptor (agonist effect) or displacement of estrogen molecules, thus reducing receptor activation (antagonistic effect) 8 .Intake of soy at high dosages may have a statistically significant reduction in breast cancer risk 9 .Epidemiological studies show that soy consumption is associated with low incidences of hormone-dependent cancers, including breast and prostate cancer in Western countries 10 .However, there are other components in soy that can also have a biological effect and there is a substitution in these countries of animal protein for soy protein.Therefore, this is still a controversial point 11 .
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".