Advanced renal cell carcinoma: what to do after first line antiangiogenic therapy?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Antiangiogenic therapy is now a standard in first line for metastatic renal cell carcinoma. Among the different options, sunitinib is particularly proposed by most guidelines, as well as a combination of bevacizumab and interferon-alfa. Defining second line therapy after these agents is dependent on indirect data since no phase III trial has specifically addressed that question. RECENT FINDINGS: The most relevant data point to everolimus that has been proven to improve PFS with acceptable toxicity compared with best supportive care in a phase III trial. Most studies on the use of antiangiogenic therapies after failure of a first one seem to demonstrate, more than anything else, that the most effective antiangiogenic therapy should be used up front since limited benefit is expected in second line. SUMMARY: This paper presents some relevant data to help recommend the most appropriate second-line therapy. The quality of data on everolimus is sufficient to propose its use for most patients in the second line setting after failure of a vascular endothelial growth factor receptor-tyrosine kinase inhibitor (VEGFR-TKI). Studies on a second line of antiangiogenic therapy demonstrate limited efficacy that does not lead to a recommendation of adequacy for most patients.
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) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".