The role of bevacizumab in colorectal cancer: understanding its benefits and limitations
Bibliographic record
Abstract
INTRODUCTION: Angiogenesis is a key factor in the development of aberrant blood vessels required for malignant growth, invasion and progression. Inhibiting VEGF is by far the most clinically advanced anti-angiogenic target. Bevacizumab (BV), the only humanized mAb directed against VEGF, is approved for use in multiple tumor types after successful clinical trial results demonstrated benefits in progression-free survival and/or overall survival when combined with common cytotoxic chemotherapies. AREAS COVERED: The review focuses on the use of BV in colorectal cancer, discusses the clinical trial data supporting its increasing use and explores its limitations. Readers will gain a succinct description of the trial data demonstrating a modest survival benefit in metastatic colorectal cancer (mCRC) and the lack of benefit of BV when utilized in the adjuvant setting. A review of common BV toxicities and a discussion about possible BV resistance mechanisms are also provided. EXPERT OPINION: Although BV has demonstrated efficacy in mCRC, there is an urgent need to improve the understanding of its mechanism of action and the development of BV resistance. Furthermore, there is a need for delineating predictive markers of BV efficacy and toxicity.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".