Bibliographic record
Abstract
behalf of the MAP.3 consortium Several large phase III trials have demonstrated that tamoxifen—and more recently, raloxifene—can effectively reduce the incidence of invasive breast cancer by 50%. However, these selective estrogen receptor modulators can also be associated with several rare, but serious, adverse events. Recently, the third-generation aromatase inhibitors (AIs) have demonstrated excellent efficacy in adjuvant breast cancer trials, and they show particular promise in the breast cancer prevention setting. The National Cancer Institute of Canada Clinical Trials Group (NCIC CTG) has developed a randomized phase III study to determine the efficacy of an AI (exemestane) to reduce the incidence of invasive breast cancer in postmenopausal women at an increased risk for developing breast cancer. The NCIC CTG MAP.3 (ExCel) trial is a double-blind placebocontrolled multicentre, multinational trial. Based on the known preclinical and clinical profile of the AIs, a greater reduction in breast cancer incidence with fewer side effects is hypothesized with this class of agents than with tamoxifen or raloxifene. KEY WORDS Aromatase inhibitors, breast cancer risk reduction, chemoprevention 1.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.073 | 0.017 |
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".