Cabozantinib Versus Standard-of-Care Comparators in the Treatment of Advanced/Metastatic Renal Cell Carcinoma in Treatment-naïve Patients: a Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Cabozantinib has recently been evaluated as a first-line treatment in advanced renal cell carcinoma (aRCC). OBJECTIVE: To indirectly assess efficacy of cabozantinib versus standard-of-care (SoC) comparators in the first-line treatment of aRCC. METHODS: We conducted a systematic literature review (SLR) to identify randomized controlled studies in the first-line setting for aRCC. The outcomes analyzed were overall survival (OS) and progression-free survival (PFS). A network meta-analysis (NMA) was conducted comparing OS and PFS hazard ratios (HRs). RESULTS: Thirteen studies were identified in the SLR to be eligible for inclusion in the NMA. The overall study populations were heterogeneous in terms of risk groups; some studies included favorable risk patients. In intermediate-risk patients, HRs (95% confidence interval) for PFS were 0.52 (0.33, 0.82), 0.46 (0.26, 0.80), 0.20 (0.12, 0.36), and 0.37 (0.20, 0.68) when cabozantinib was compared with sunitinib, sorafenib, interferon (IFN), or bevacizumab plus IFN, respectively. In poor-risk patients, the NMA also demonstrated significant superiority in terms of PFS for cabozantinib; HRs were 0.31 (0.11, 0.90), 0.22 (0.06, 0.87), 0.16 (0.04, 0.64), and 0.20 (0.05, 0.88), when cabozantinib was compared with sunitinib, temsirolimus, IFN, or bevacizumab plus IFN, respectively. When the overall study populations were compared, the results were similar to the subgroup analyses. OS HRs in all analyses favored cabozantinib, but were not statistically significant. CONCLUSIONS: The results suggest that cabozantinib significantly increases PFS in intermediate-, and poor-risk subgroups when compared to standard-of-care comparators. Although overall populations included favorable risk patients in some studies, the results seen were consistent with the subgroup analyses.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".