An update on adjuvant systemic therapy for elderly patients with early breast cancer
Bibliographic record
Abstract
INTRODUCTION: Elderly women with early breast cancer require an individualized approach to risk assessment and treatment. Unfortunately, there are limited data to inform optimal adjuvant therapy decisions in this population. Cytotoxic chemotherapy, biologic treatments and endocrine agents, while important in reducing breast cancer recurrence and mortality, are associated with the potential for adverse effects that may be of particular significance to elderly patients. AREAS COVERED: In this review, we summarize the evidence for geriatric assessment in elderly patients with early breast cancer, outline special considerations for the use of chemotherapy and trastuzumab in older adults, and describe the age-specific risks of endocrine therapy in the adjuvant breast cancer setting. EXPERT OPINION: The treatment of elderly women with early breast cancer should take into account cancer risk, life expectancy, comorbidities, functional status, physiologic changes, and patient values. Formal geriatric assessment may better inform treatment recommendations for individual patients. In general, there is no strong evidence to suggest that older women benefit less from standard adjuvant therapies than do their younger counterparts. When choosing between endocrine therapies, the differential risks associated with each agent should be considered and particular attention to the fracture risk on aromatase inhibitors (AIs) is warranted. Enrolment of women over 70 years of age into breast cancer clinical trials should be encouraged to better inform treatment guidelines.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".