Aromatase inhibitors for prevention of breast cancer in postmenopausal women
Bibliographic record
Abstract
OBJECTIVE: The increasing incidence of breast cancer (BC) worldwide has resulted in widespread interest in primary prevention therapies. A number of large randomized trials have shown that selective estrogen receptor modulators can reduce the relative risk for BC by 30% to 40% in high-risk women. In early-stage BC, aromatase inhibitors (AIs) showed a 35% relative reduction in the risk of contralateral BCs compared with tamoxifen. In this narrative review, we discuss the role of AIs in the primary prevention of BC and novel research on combination hormone therapy-medical therapy for the primary prevention of BC. METHODS: Using PubMed/Medline, we comprehensively searched for studies of BC primary prevention using AIs, including studies of novel methods of prevention using combination hormone therapy-BC prevention. RESULTS: Two large multicenter, prospective, randomized, placebo-controlled trials have evaluated AIs--anastrozole (International Breast Cancer Intervention Study II) and exemestane (Mammary Prevention 3)--for BC risk reduction in women at increased risk for BC, which we summarize. We identified five studies (three completed and two ongoing) of combination AI-hormone therapy that are undergoing investigation for BC risk reduction. CONCLUSIONS: AIs are effective at BC risk reduction, although long-term follow-up data are required to assess whether this risk reduction will result in reduced mortality. Combination hormone therapy-AI for BC risk reduction is experimental and warrants further investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".