Extended adjuvant endocrine therapy – A standard to all or some?
Bibliographic record
Abstract
Patients with estrogen receptor-positive (ER +) early breast cancer (EBC) are at a continuous risk for distant relapse despite 5 years of standard endocrine therapy, even after 10-15 years after primary diagnosis. Hence, large randomized clinical trials were conducted to evaluate the role of extended endocrine treatment (ET) with the primary goal to prevent or at least delay distant relapse. Two very large trials of extended tamoxifen (TAM), the ATLAS and the aTTom trial, proved the efficacy of prolonged TAM particularly important after 10 years due to the carry-over effect of the five initial years. Additionally, the extended use of AIs after 5 years of tamoxifen, also proved to be efficacious in preventing late distant relapses. For letrozole (LET) it was shown in the MA.17 trial that it also improves overall survival (OS) in node-positive BC patients. There are many options and still unanswered questions related to extended ET, which are discussed in this review. The most important issue in deciding prolonged duration of ET is undoubtfully how to identify ER+ patients who benefit most from this approach. With this purpose, not only classical pathological factors have been studied, but also molecular profiles of individual tumors, which might help us in the near future to better tailor ET. Not only efficacy, but also toxicity of such prolonged treatment is essential for optimal use, particularly maintained compliance in a routine clinical practice. These issues are discussed in this review.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".