Breast Cancer Prevention with Selective Estrogen Receptor Modulators
Bibliographic record
Abstract
Chemoprevention for breast cancer is both old and new. It has long been appreciated that early ovarian ablation dramatically reduces the incidence of breast cancer in premenopausal women. It was subsequently demonstrated, in the Early Breast Cancer Trialists' Collaborative Group (EBCTCG) overview, that tamoxifen results in a 40% or greater reduction in the incidence of contralateral breast cancer. Now, the National Surgical Adjuvant Breast and Bowel Project (NSABP) has shown a similar reduction in a randomized trial [Breast Cancer Prevention Trial (BCPT)] comparing tamoxifen and placebo in women aged 35 years or over at increased risk of developing breast cancer because of age, family history, or other factors. In this trial, the incidences of both ductal carcinoma in situ (DCIS) and invasive cancer were reduced. Reduction in incidence was similar over all years of the study and in all subgroups of high-risk women. However, all of the reduction was confined to estrogen receptor (ER)-positive tumors. Raloxifene, a newer selective estrogen receptor modulator (SERM) originally developed for osteoporosis, also appears to have a major preventive effect on breast cancer incidence. Limitations in the design and patient population of raloxifene trials, however, have made it difficult to as yet recommend raloxifene for risk reduction of breast cancer. The randomized Study of Tamoxifen and Raloxifene (STAR) study, which will compare raloxifene to tamoxifen in over 20,000 postmenopausal women at increased risk of breast cancer, as well as ongoing and proposed placebo-controlled studies of tamoxifen, the aromatase inhibitor anastrazole, and other antiestrogens in high- or average-risk postmenopausal women, will provide further results on optimal prevention strategies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".