Sunitinib therapy for metastatic renal cell carcinoma: recommendations for management of side effects
Bibliographic record
Abstract
Sunitinib, a new vascular endothelial growth factor receptor inhibitor, has demonstrated high activity in renal cell carcinoma (RCC) and is now widely used for patients with metastatic disease. Although generally well tolerated and associated with a low incidence of common toxicity criteria grade 3 or 4 toxicities, sunitinib exhibits a distinct pattern of novel side effects that require monitoring and management. This article summarizes the most important side effects and proposes recommendations for their monitoring, prevention and treatment, based on the existing literature and on suggestions made by an expert group of Canadian oncologists. Fatigue, diarrhea, anorexia, oral changes, skin toxicity and hypertension seem to be the most clinically relevant toxicities of sunitinib. Fatigue may be partly related to the development of hypothyroidism during sunitinib therapy for which patients should be observed and, if necessary, treated. Hypertension can be treated with standard antihypertensive therapy and rarely requires treatment discontinuation. Neutropenia and thrombocytopenia usually do not require intervention, in particular no episodes of neutropenic fever have been reported to date. A decrease in left ventricular ejection fraction is a rare, but potentially life-threatening side effect. Because of its metabolism by cytochrome P450 3A4 a number of drugs can potentially interact with sunitinib. Clinical response and toxicity should be carefully observed when sunitinib is combined with either a cytochrome P450 3A4 inducer or inhibitor and doses adjusted as necessary. Knowledge about side effects, as well as the proactive assessment and consistent management of sunitinib-related side effects, is critical to ensure optimal benefit from sunitinib treatment.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".