Issues regarding improving the impact of antiangiogenic drugs for the treatment of breast cancer
Bibliographic record
Abstract
One of the major recent clinical advances in cancer treatment is the use of antiangiogenic drugs such as bevacizumab, sorafenib, and sunitinib. Bevacizumab, the monoclonal anti-VEGF antibody, has been approved for the first line treatment of metastatic breast cancer (MBC) when combined with taxane. However, the clinical benefits are modest; despite a doubling of response rates and significant prolongation of progression free survival times, no increase in overall survival is attained. This review summarizes some of the possibilities to account for this discrepant result. These include rapid development of acquired drug resistance due to the redundancy of proangiogenic growth factors, acceleration of tumor growth after antiangiogenic drug treatments are stopped, and increases in tumor cell malignant aggressiveness driven by mechanisms such as increased tumor hypoxia. Some possible strategies to improve the benefits of antiangiogenic drug therapy are discussed such as prolonging the treatment beyond tumor progression, combination with other therapeutic modalities, e.g. long term ('maintenance') low-dose metronomic chemotherapy or additional targeted/biologic drugs, e.g. trastuzumab.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".