Comparison of First-line Pazopanib and Sunitinib in Metastatic Renal Cell Carcinoma: Experiences of the Urologic Cancer Centre for Research and Innovation
Bibliographic record
Abstract
Abstract Background: Sunitinib and pazopanib are orally-administered tyrosine kinase receptor inhibitors (TKIs) approved as first-line therapy for the treatment of metastatic renal cell carcinoma (mRCC). The IMDC criteria are a predictive prognostic model for patients with mRCC when stratified into three prognosis groups: favourable, intermediate and poor. We retrospectively compared the efficacy and safety of sunitinib and pazopanib as first-line therapy for patients with mRCC in our single institution database. Methods: Retrospective analysis was done to compare progression-free survival (PFS) and side effects of sunitinib and pazopanib as first-line therapy in patients with mRCC. Patients were stratified into prognosis groups according to IMDC criteria. Disease assessment was performed on measurable aspects of disease based on computed tomography or magnetic resonance imaging reports. Survival analysis was performed using the Kaplan-Meier method and Cox regression, with disease progression as the endpoint.Results: Data was obtained from 228 patients with mRCC who were treated with either pazopanib (n=57) or sunitinib (n=171). No significant difference in PFS was found between sunitinib and pazopanib (HR for disease progression or all-cause death, 1.10; 95%CI: 0.76-1.57, p=0.62). Median PFS time for patients receiving sunitinib was 9.4 months and for pazopanib, 8.5 months. Median PFS for patients with intermediate-risk disease was similar between groups (9.4 months vs. 9.2 months, respectively, p=0.93). However, patients treated with sunitinib experienced a greater number of side effects compared to pazopanib. Conclusions: Sunitinib and pazopanib are similarly efficacious as first-line therapy for mRCC. However, adverse events are lower with pazopanib.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".