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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.031 | 0.001 |
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; both teacher heads agree on what is shown here.
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".