Redrawing the Lines: The Next Generation of Treatment in Metastatic Breast Cancer
Bibliographic record
Abstract
Although not considered curative in nature, new therapeutic advances in metastatic breast cancer (MBC) have substantially improved patient outcomes. This article discusses the state-of-the-art and emerging therapeutic options for management of MBC. BC systemic therapy targets multiple key pathways, including estrogen receptor signaling, HER2 signaling, and phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling. Other therapeutic strategies include targeting DNA repair, inhibiting immune checkpoints, and developing antibody-drug conjugates. Although surgery historically was reserved for palliation of symptomatic, large, or ulcerating masses, some data suggest a possibly expanding role for more aggressive locoregional therapy in combination with systemic therapy. As technology develops, biomarker-specific, line-agnostic, and receptor-agnostic treatment strategies will redraw the current lines of MBC care. However, tumor heterogeneity remains a challenge. To effectively reshape our approach to MBC, careful consideration of the patient perspective, the costs and value of novel treatments, and accessibility (especially in developing countries) is paramount.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".