Bibliographic record
Abstract
BACKGROUND: Many women at increased risk for breast cancer could benefit from preventive therapy. Preventive therapy options for breast cancer risk reduction have expanded in the last few years to include both selective receptor modulators (tamoxifen and raloxifene) and aromatase inhibitors (anastrozole and exemestane). METHODS: Risk factors that place women at high risk for breast cancer, as well as risk calculation models appropriate for the selection of candidates for preventive therapy, are presented, followed by a review of current guidelines for chemoprevention and results of chemoprevention trials. RESULTS: The modified Gail model or Breast Cancer Risk Assessment Tool is the most widely utilized risk assessment calculator to determine eligibility for chemoprevention. Women most likely to benefit from preventive therapy include those at high risk under the age of 50 years and those with atypical hyperplasia. Physician and patient barriers limit widespread acceptance and adherence to preventive therapy. CONCLUSIONS: Published guidelines on chemoprevention for breast cancer have been updated to increase awareness and encourage discussion between patients and their physicians regarding evidence-based studies evaluating the benefits of preventive options for women at increased risk for breast cancer. However, even with increasing awareness and established benefits of preventive therapy, the uptake of chemoprevention has been low, with both physician and patient barriers identified. It is prudent that these barriers be overcome to enable high-risk women with a favorable risk-to-benefit ratio to be offered chemoprevention to reduce their likelihood of developing hormone receptor-positive breast cancer.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".