Anti-Angiogenic Targets in the Treatment of Advanced Renal Cell Carcinoma
Bibliographic record
Abstract
Drugs that target the vascular endothelial growth factor (VEGF) and platelet derived growth factor (PDGF) pathways have revolutionized the treatment of patients with metastatic renal cell cancer (RCC). Patients with clear cell RCC often have mutations or silencing of the von Hippel Lindau gene leading to an accumulation of HIF 1 alpha. This allows growth factors such as VEGF and PDGF to be upregulated to promote angiogenesis and endothelial stabilization. Both sunitinib and sorafenib target VEGF and PDGF receptor tyrosine kinases while bevacizumab is a monoclonal antibody to VEGF. These three agents have demonstrated superior progression free survival in patients with metastatic RCC when compared to interferon or placebo. Newer anti-VEGF agents such as axitinib, pazopanib and cediranib are currently under investigation to elucidate future treatment options. The mammalian target of rapamycin (mTOR) is downstream of the VEGF pathway and has been targeted with drugs including temsirolimus and everolimus. This review will detail the pharmacologic and molecular activity of these agents and how they translate into clinical efficacy.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".