Effects of Tamoxifen vs Raloxifene on the Risk of Developing Invasive Breast Cancer and Other Disease Outcomes. The NSABP Study of Tamoxifen and Raloxifene (STAR) P-2 Trial
Bibliographic record
Abstract
The selective estrogen receptor modulator (SERM) tamoxifen has long been used to treat both early and advanced breast cancer. Raloxifene is a second-generation SERM that, in addition to combatting osteoporosis, may also lessen the risk of invasive breast cancer in postmenopausal women. The National Surgical Adjuvant Breast and Bowel Project (NSABP) Study of Tamoxifen and Raloxifene (STAR) is a prospective, randomized, double-blind trial conducted at nearly 200 clinical centers throughout North America. Participating were 19,747 postmenopausal women whose mean age was 58.5 years and whose 5-year breast cancer risk was increased at 4.03%. The minimal 5-year risk for entering the trial was 1.66%. The women received either 20 mg tamoxifen or 60 mg raloxifene daily for 5 years. During a mean follow up of 3.9 years, there were 163 cases of invasive breast cancer in women assigned to receive tamoxifen and 268 in those assigned to raloxifene, for respective incidence rates of 4.3 and 4.4 per 1000 and a risk ratio (RR) of 1.02 (95% confidence interval [CI], 0.82–1.28). Noninvasive breast cancers were less numerous in the tamoxifen group (RR, 1.40; 95% CI, 0.98–2.00), but uterine cancers were more frequent in this group (RR, 0.62; 95% CI, 0.35–1.08). No group differences were documented for invasive cancer at other sites, ischemic heart disease events, or strokes. Thromboembolic events were less frequent in raloxifene-treated women. Women in the 2 treatment groups had similar numbers of osteoporotic fractures. Those taking raloxifene had fewer cataracts and underwent fewer cataract surgeries. There was no difference in total deaths, and causes of death were similarly distributed in the 2 groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| 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".