Sunitinib in Metastatic Renal Cell Carcinoma: Recommendations for Management of Noncardiovascular Toxicities
Bibliographic record
Abstract
The multitargeted tyrosine-kinase inhibitor sunitinib has emerged as one of the standards of care for good- and intermediate-risk metastatic renal cell carcinoma. Although generally associated with acceptable toxicity, sunitinib exhibits a novel and distinct toxicity profile that requires monitoring and management. Fatigue, diarrhea, anorexia, oral changes, hand-foot syndrome and other skin toxicity, thyroid dysfunction, myelotoxicity, and hypertension seem to be the most common and clinically relevant toxicities of sunitinib. Drug dosing and treatment duration are correlated with response to treatment and survival. Treatment recommendations for hypertension have been published but, currently, no standard guidelines exist for the management of noncardiovascular side effects. To discuss the optimal management of noncardiovascular side effects, an international, interdisciplinary panel of experts gathered in November 2009. Existing literature on incidence, severity, and underlying mechanisms of side effects as well as on potential treatment options were carefully reviewed and discussed. On the basis of these proceedings and the thorough review of the existing literature, recommendations were made for the monitoring, prevention, and treatment of the most common noncardiovascular side effects and are summarized in this review. The proactive assessment and consistent and timely management of sunitinib-related side effects are critical to ensure optimal treatment benefit by allowing appropriate drug dosing and prolonged treatment periods.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".