Primary endocrine therapy as an approach for patients with localized breast cancer deemed not to be surgical candidates
Bibliographic record
Abstract
PURPOSE OF REVIEW: For women diagnosed with localized hormone-receptor-positive breast cancer who have a poor performance status or who have medical conditions precluding aggressive treatment with chemotherapy or surgery, primary endocrine therapy has been proposed as a therapeutic alternative. Given that society is rapidly aging overall, this subset of patients will likely become a greater proportion of the patient population seen by breast cancer specialists. RECENT FINDINGS: On the basis of the results from randomized trials in patients whose health does not permit surgery, it appears that tamoxifen achieves a similar overall survival compared with surgery plus tamoxifen, supporting the use of primary endocrine therapy. In the neoadjuvant setting, aromatase inhibitors appear superior to tamoxifen, suggesting that these agents may be the best choice in the primary endocrine therapy setting. In addition, new breakthroughs for the management of hormone-receptor-positive disease in the metastatic setting have recently been reported. SUMMARY: This review will discuss the rationale and evidence for primary endocrine therapy; which agents could be selected for use; and how recent advances for the management of hormone-receptor-positive disease may potentially apply to this population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".