Endocrine Therapy for Breast Cancer in the Primary Care Setting
Bibliographic record
Abstract
The treatment of hormone-positive breast cancer (bca) is a rapidly evolving field. Improvement in the understanding of the mechanisms of action and resistance to anti-hormonal therapy has translated, in the past decade, into multiple practice-changing clinical trials, with the end result of increased survivorship for patients with all stages of hormone-positive cancer. The primary care physician will thus play an increasing role in the routine care, surveillance, and treatment of issues associated with anti-hormonal therapy. The aim of the present review was to provide a focused description of the issues relevant to primary care, while briefly highlighting recent advances in the field of anti-hormonal therapy. Key Points: ■ Hormone-positive bca is the most prevalent form of bca and, compared with the other subtypes, is usually associated with better survival.■ Survivorship has significantly increased for all stages of hormone-positive bca, making the primary care physician a key player in the care of affected patients.■ The two most common classes of anti-hormonal agents used in these patients are selective estrogen receptor modulators and aromatase inhibitors. Each class of medication is associated with signature side effects.■ Within the past decade, multiple novel estrogen receptor blockers (for example, fulvestrant) and agents aimed at circumventing resistance to endocrine therapy [inhibitors of cyclin-dependent kinase 4/6 and of mtor (the mechanistic target of rapamycin)] have gained clinical ground. Understanding their side effects will be increasingly relevant to primary care physicians.■ Multidisciplinary care is always encouraged in the care of cancer patients receiving anti-hormonal 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".