Review: Targeted therapy for metastatic renal cell carcinoma: current treatment and future directions
Bibliographic record
Abstract
An understanding of vascular endothelial growth factor (VEGF) and mammalian target of rapamycin (mTOR) pathways has greatly changed the way metastatic renal cell carcinoma (RCC) is treated. Based on available phase III randomized trials, anti-VEGF agents such as sunitinib, sorafenib, bevacizumab-based therapy, and mTOR-targeted agents such as temsirolimus and everolimus have been used in the treatment armamentarium for this disease. Now that agents directed against these pathways have largely replaced immunotherapy as the standard of care, new questions have emerged and are the subject of ongoing clinical trials. The development of new targeted therapies including axitinib, pazopanib, cediranib, volociximab, tivozanib (AV-951), BAY 73-4506, and c-met inhibitors such as GSK1363089 and ARQ197 may potentially expand the list of treatment options. Sequential and combination targeted therapies are currently under investigation in advanced disease as are adjuvant and neo-adjuvant approaches around nephrectomy.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".