Bibliographic record
Abstract
INTRODUCTION: Over the past decade, a greater understanding into the molecular pathogenesis of renal cell carcinoma (RCC) has led to major advances in treatment options. Sunitinib is an oral, small-molecule, multi-targeted receptor tyrosine kinase inhibitor (TKI) that targets a number of receptors, including vascular endothelial growth factor receptors (VEGFR) and platelet-derived growth factor receptors (PDGFR). Sunitinib was one of the first targeted agents studied in metastatic RCC (mRCC) and is currently used worldwide in the management of mRCC. AREAS COVERED: This drug evaluation addresses the preclinical and clinical development of sunitinib. It provides an in-depth discussion of the Phase II data that led to its approval in mRCC and the subsequent Phase III clinical trial comparing sunitinib to interferon-α. More recent data from the large international expanded access trial, in non-clear cell carcinoma patients, different dosing schedule studies and safety issues are also discussed. Finally, areas for the future use of sunitinib, including in the adjuvant setting, are reviewed. EXPERT OPINION: Since the FDA approved sunitinib for advanced RCC in January 2006, much more has been learned about its efficacy and tolerability. Over the past decade of its clinical use, it has become clear that expertise is required when prescribing sunitinib, in terms of maximizing dose, anticipating and managing side effects, and assessing responses. In the future, a better understanding of sunitinib's role compared with other VEGF TKIs and mTOR inhibitors, and in other roles such as the adjuvant setting or in non-clear cell pathology, will become evident.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.008 | 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".