A review of current and emerging therapies in breast cancer: implications for older adults
Bibliographic record
Abstract
INTRODUCTION: Breast cancer is the most common cancer in women. As global populations age, the number of older adults with breast cancer is expected to increase significantly. Recently, progress has been made across all subtypes of breast cancer with the development of new targeted therapeutics and the success of novel combinations. However, treating older adults remains challenging due to the limited representation of this population in clinical trials, resulting in a lack of robust data on treatment efficacy and tolerability. AREAS COVERED: We reviewed FDA-approved therapies for breast cancer since 2020. We analyzed pivotal clinical trials leading to registration, as well as subanalyses of older populations and real-world studies for each approved therapy, with a focus on their impact in older adults. EXPERT OPINION: Breast cancer is a quickly changing field with many new therapeutics and novel combinations on the horizon. With each new approval, it is critical to assess expected side effects and approach each patient with an individualized plan. Many clinical trials enroll a small number of older adults, making it difficult to extrapolate data to geriatric populations. Future trials should incorporate broader eligibility criteria and include geriatric-specific endpoints to better inform treatment decisions for older adults with breast cancer.
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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) | 0.001 | 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 | 0.001 |
| 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".