Contemporary treatment of metastatic renal cell carcinoma
Bibliographic record
Abstract
The introduction of targeted therapy has revolutionized the treatment of patients with metastatic renal cell carcinoma (mRCC). The current standard of care focuses on the inhibition of angiogenesis through the targeting of the vascular endothelial growth factor receptor (VEGFR) and the mammalian target of rapamycin (mTOR). Over the past few years, research exploring novel targeted agents has blossomed, leading to the approval of various targeted therapies. Furthermore, results from the CheckMate025 and the METEOR trials have brought about two additional novel options: the programmed cell death 1 (PD-1) checkpoint inhibitor nivolumab and the MET/VEGFR/AXL inhibitor cabozantinib, respectively. With the variety of therapeutic agents available for treatment of mRCC, research examining appropriate sequencing and combinations of the drugs is ongoing. This review discusses the role of prognostic criteria, such as those from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) criteria. It also covers the current standard of treatment for mRCC with targeted therapy in first-, second-, and third-line setting. Additionally, the novel mechanism of action of nivolumab and cabozantinib, therapeutic sequencing and ongoing clinical trials are 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".