An Updated Review on the Efficacy of Adjuvant Endocrine Therapies in Hormone Receptor–Positive Early Breast Cancer
Bibliographic record
Abstract
The third-generation aromatase inhibitors (AIS) are largely replacing tamoxifen in the adjuvant treatment of early-stage breast cancer in postmenopausal women with hormone receptor-positive tumours. To date, multiple trials have been conducted comparing tamoxifen treatment with an AI, and all have demonstrated improved disease-free survival with AI treatment. Trials have included direct 5-year comparisons between tamoxifen and an AI, switching to an AI within 5 years after initial tamoxifen treatment, or extending treatment with an AI after 5 years of completed tamoxifen treatment. Some of these trials have been completed; others are ongoing; and head-to-head trial comparisons of individual AIS are also in progress. The present article summarizes the data obtained from various clinical trials of hormonal therapy for early breast cancer. It also reviews recent data so as to shed light on the current status of these therapies. The focus is on the efficacy of treatment with an AI. Toxicity is discussed in the second article in this supplement.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".