New approaches to first-line treatment of advanced renal cell carcinoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The treatment of patients with renal cell carcinoma (RCC) is evolving rapidly, with promising new regimens being developed and approved for patients with advanced disease, particularly the combination of tyrosine kinase inhibitors with immune checkpoint inhibitors. Within the last 6 months, favorable first-line setting results for patients with clear cell RCC have been reported for the combination of cabozantinib plus nivolumab in the phase III CheckMate 9ER study, leading to its regulatory approval, and lenvatinib plus pembrolizumab in the phase III CLEAR study. Additional systemic first-line treatments for clear cell RCC include axitinib plus pembrolizumab, pazopanib, and sunitinib for favorable-risk patients and ipilimumab plus nivolumab, axitinib plus pembrolizumab, axitinib plus avelumab, and cabozantinib for intermediate- or poor-risk patients. In this review of novel approaches for first-line treatment of advanced RCC, we present an overview of current treatment strategies, the basis behind emerging treatment approaches, a summary of key results from the pivotal studies using tyrosine kinase inhibitor and immune checkpoint inhibitor combination therapy, novel treatments and strategies under development, and efforts for identifying biomarkers to guide treatment decisions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it