Bibliographic record
Abstract
Renal cell cancer has been refractory to drug therapy in the large majority of patients. Targeted agents including sunitinib have been intensively evaluated in renal cell cancer over the past 5 years. Sunitinib is an oral small molecule inhibitor of several targets including multiple tyrosine kinase receptors of the angiogenesis pathway. This review surveys the rationale, development, validation, and clinical use of sunitinib that received conditional approval for use in North America and Europe in 2006. In patients with the clear-cell subtype of renal cell cancer and metastatic disease with good or moderate prognostic factors for survival, sunitinib 50 mg for 4 weeks of a 6-week cycle provides superior surrogate and patient-reported outcomes when compared with interferon-alfa, the previous commonly used first-line drug. Overall survival has not yet shown improvement over interferon and is problematic because of patient crossover from the control arm to sunitinib at disease progression. Toxicity is significant but manageable with experienced monitoring. Sunitinib therapy is an important step forward for this condition. High cost and limited efficacy support the ongoing search for further improved therapy.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".