Safety and treatment patterns of multikinase inhibitors in patients with metastatic renal cell carcinoma at a tertiary oncology center in Italy
Bibliographic record
Abstract
BACKGROUND: Multikinase inhibitors (MKIs) sunitinib and sorafenib have become a standard of care for metastatic renal cell carcinoma (mRCC). This study assessed safety and treatment patterns for these agents in a real-world clinical practice setting in Italy. METHODS: A retrospective medical record review was performed at a tertiary oncology center in Italy. The study included MKI-naïve non-trial patients ≥18 years old, with a histological diagnosis of mRCC, and who received sunitinib or sorafenib as first MKI during 9/2005-7/2008. Data were collected on adverse events (AEs), treatment modifications (discontinuations, interruptions, dose changes), and reasons for these modifications. RESULTS: 145 patients were included; 85 received sunitinib and 60 received sorafenib as first-line MKI. Median treatment duration was 6.6 (sunitinib) and 5.8 (sorafenib) months. 97.6% and 70.0% of patients receiving sunitinib and sorafenib, respectively, experienced ≥1 AE; 27.1% and 31.7% had ≥1 grade 3/4 AE. The most common any grade AE for sunitinib was fatigue/asthenia (81.2%), followed by mucositis/stomatitis (58.8%) and decreased taste sensation (42.4%), while for sorafenib this was fatigue/asthenia (43.3%) followed by hand-foot syndrome (38.3%) and diarrhea (31.7%). Treatment discontinuation, interruption, and dose reduction due to AEs occurred in 11.8%, 23.5%, and 30.6%, respectively, of patients receiving sunitinib, and 5.0%, 23.3%, and 36.7%, respectively, of patients receiving sorafenib. CONCLUSIONS: In this retrospective study, most patients experienced ≥1 AE during first-line MKI treatment. AEs were reported frequently and resulted in treatment modifications in 40% of patients receiving sunitinib and 45% of patients receiving sorafenib. These results suggest a need for additional effective and more tolerable treatments for mRCC.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".