Appraising Adjuvant Endocrine Therapy in Hormone Receptor Positive HER2-Negative Breast Cancer—A Literature Review
Bibliographic record
Abstract
BACKGROUND: Approximately 75% of breast cancer (BC) is associated with luminal differentiation expressing endocrine receptors (ER). For ER+ HER2- tumors, adjuvant endocrine therapy (ET) is the cornerstone treatment. Although relapse events steadily continue, the ET benefits translate to dramatically lengthen life expectancy with bearable side-effects. This review of ER+ HER2- female BC outlines suitable adjuvant treatment strategies to help guide clinical decision making around appropriate therapy. METHODS: A literature search was conducted in Embase, Medline, and the Cochrane Libraries, using ER+ HER-, ET BC keywords. RESULTS: In low-risk patients: five years of ET is the standard option. While Tamoxifen remains the preferred selection for premenopausal women, AI is the choice for postmenopausal patients. In the high-risk category: ET plus/minus OFS with two years of Abemaciclib is recommended. Although extended ET for a total of ten years is an alternative, the optimal AI duration is undetermined; nevertheless an additional two to three years beyond the initial five years may be sufficient. In this postmenopausal group, bisphosphonate is endorsed. CONCLUSIONS: Classifying the risk category assists in deciding the treatment route and its optimal duration. Tailoring the breadth of ET hinges on a wide array of factors to be appraised for each individualized case, including weighing its benefits and harms.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".