American Society of Clinical Oncology Clinical Practice Guideline: Update on Adjuvant Endocrine Therapy for Women With Hormone Receptor–Positive Breast Cancer
Bibliographic record
Abstract
PURPOSE: To develop evidence-based guidelines, based on a systematic review, for endocrine therapy for postmenopausal women with hormone receptor-positive breast cancer. METHODS: A literature search identified relevant randomized trials. Databases searched included MEDLINE, PREMEDLINE, the Cochrane Collaboration Library, and those for the Annual Meetings of the American Society of Clinical Oncology (ASCO) and the San Antonio Breast Cancer Symposium (SABCS). The primary outcomes of interest were disease-free survival, overall survival, and time to contralateral breast cancer. Secondary outcomes included adverse events and quality of life. An expert panel reviewed the literature, especially 12 major trials, and developed updated recommendations. RESULTS: An adjuvant treatment strategy incorporating an aromatase inhibitor (AI) as primary (initial endocrine therapy), sequential (using both tamoxifen and an AI in either order), or extended (AI after 5 years of tamoxifen) therapy reduces the risk of breast cancer recurrence compared with 5 years of tamoxifen alone. Data suggest that including an AI as primary monotherapy or as sequential treatment after 2 to 3 years of tamoxifen yields similar outcomes. Tamoxifen and AIs differ in their adverse effect profiles, and these differences may inform treatment preferences. CONCLUSION: The Update Committee recommends that postmenopausal women with hormone receptor-positive breast cancer consider incorporating AI therapy at some point during adjuvant treatment, either as up-front therapy or as sequential treatment after tamoxifen. The optimal timing and duration of endocrine treatment remain unresolved. The Update Committee supports careful consideration of adverse effect profiles and patient preferences in deciding whether and when to incorporate AI therapy.
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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 | 0.022 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".