Management of Toxicity and Side Effects of Systemic Therapy for Renal Cell Carcinoma
Bibliographic record
Abstract
Standard approved systemic treatment options for the management of renal cancer have entirely transformed in the last 15 years and now comprise molecularly targeted therapies against the vascular endothelial growth factor receptor (VEGFR) and the mammalian target of rapamycin (mTOR) as well as immune checkpoint inhibitors. These agents may be used alone as monotherapies but increasingly are used in various combinations. The associated important improvements in cancer control and survival have therefore been accompanied by a range of new toxicities. Good management of these toxicities is important for patient safety and quality of life, and also to optimize patients’ opportunity to continue with and therefore benefit from these therapies. The most common toxicities associated with VEGFR tyrosine kinase inhibitors are fatigue, skin rashes, gastrointestinal, stomatitis, hypertension and other cardiovascular toxicities, and hematological and endocrine dysfunction. Common side effects of mTOR inhibitors include asthenia, stomatitis, skin rashes, pneumonitis, metabolic changes and infections. Checkpoint inhibitors can lead to toxicities of any organ system with those seen most frequently including dermatologic, gastrointestinal and hepatic, endocrine, musculoskeletal, and pulmonary, whilst renal, hematological, ophthalmic, cardiac and neurological toxicities are seen less often. In general terms, toxicity management should start preemptively with patient education and may also include a combination of supportive approaches, dose reduction, schedule alteration, treatment interruption and occasionally treatment cessation. Treatment of individual toxicities is dependent on the likely causative agent and is guided by its grade or severity. Specific recommendations for management are discussed in this chapter.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".