First-line sunitinib versus pazopanib in metastatic renal cell carcinoma (mRCC): Results from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC).
Bibliographic record
Abstract
544 Background: Sunitinib (SU) and Pazopanib (PZ) have been compared head-to-head in the first-line phase III COMPARZ study in metastatic renal cell carcinoma (mRCC). We compared SU versus PZ, to confirm outcomes and subsequent second-line therapy efficacy in a population-based setting. Methods: We used the IMDC to assess overall survival (OS), progression-free survival (PFS), response rate (RR) and performed proportional hazard regression adjusting for IMDC prognostic groups. Second-line OS2 and PFS2 were also evaluated. Results: We obtained data from 3,606 patients with mRCC treated with either first line SU (n=3226) or PZ (n=380) with an overall median follow-up of 43.5 months (m) (CI95% 41.4 – 46.4). IMDC risk group distribution for favorable prognosis was 440 (17.3%) for SU vs 72 (25%) for PZ, intermediate prognosis 1414 (55.6%) for SU vs 153 (53%) for PZ, poor prognosis 689 (27.1%) for SU vs 62 (22%) for PZ, p= 0.0027. We found no difference between SU vs. PZ for OS (20.1 [CI95% 18.76-21.42] vs. 23.68 m [CI95% 19.54 - 28.81] p=0.19), PFS (7.22 [CI95% 6.76 - 7.78] vs. 6.83 m [CI95% 5.58 - 8.27] p=0.49). The RR was similar in both groups (Table 1). Adjusted HR for OS and PFS were 0.952 (CI95% 0.788 – 1.150 p=0.61) and 1.052 (CI95% 0.908 – 1.220 p = 0.49), respectively. We also found no difference in any second-line treatment between either post-SU vs. post-PZ groups for OS2 (12.88 [CI95% 11.89 – 14.19] vs. 12.91 m [CI95% 10.3 – 19.1] p=0.47) and PFS2 (3.67 [CI95% 3.38 – 3.87] vs. 4.53 m [CI95% 3.08 – 5.35] p=0.4). There was no statistical difference in OS2 and PFS2 if everolimus was used after SU or PZ (p = 0.33 and p = 0.41, respectively) or if axitinib was used after SU or PZ (p = 0.73 and p = 0.72, respectively). Conclusions: We confirmed in real world practice, that SU and PZ have similar efficacy in the first-line setting for mRCC and do not affect outcomes with subsequent second-line treatment. [Table: see text]
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".