Bibliographic record
Abstract
Phase III trials of antiangiogenic drugs for metastatic breast cancer have either had only limited success, e.g. the monoclonal anti-VEGF antibody bevacizumab when used with various conventional chemotherapy regimens, or have failed altogether, e.g. the small molecule oral tyrosine kinase inhibitor (TKI) sunitinib. No phase III trial has yet demonstrated an overall survival benefit and the progression free survival (PFS) benefits, when attained with bevacizumab are short, with perhaps one exception. Together, these results call for a reappraisal of using antiangiogenic drugs for breast cancer and possible strategies to improve their efficacy. Among the reasons to help explain the limited benefits observed thus far include the possibility that angiogenesis may not be a major driver of breast cancer growth, compared to some other types of cancer; that acquired resistance may develop rapidly to VEGF-pathway targeting antiangiogenic drugs, in part due to angiogenic growth factor redundancy; that optimal chemotherapy regimens have not been used in conjunction with an antiangiogenic drug; and that antiangiogenic drugs may secondarily aggravate biologic aggressiveness of the tumors, thereby reducing their overall efficacy after inducing an initial benefit. Several possible strategies are discussed for improving the efficacy of antiangiogenic drugs, including combination with different chemotherapy regimens, e.g. long term and less toxic metronomic chemotherapy protocols; validation of predictive biomarkers to individualize patient therapy; development of improved preclinical therapy models, e.g. involving advanced metastatic breast cancer, and combination with other types of anti-cancer agents especially biologies such as trastuzumab for Her2-positive breast cancer. Reasons for the current concern regarding use of antiangiogenic drug treatments for early stage cancers, including breast cancer, are also discussed.
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 | 0.003 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".