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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".