Adjuvant Systemic Treatment for Renal Cancer After Surgery: A Network Meta-Analysis
Bibliographic record
Abstract
Background Approximately 15% to 20% of patients will experience disease recurrence following surgical removal of renal cell carcinoma. A range of pharmacological agents is prescribed for metastatic renal cell carcinoma, but there are trials testing whether these have an earlier role in the adjuvant setting. We aim to assess the efficacy of adjuvant systemic treatment following surgery in patients with renal cell carcinoma and to determine the most effective treatment. Methods The protocol for this review was published in PROSPERO (CRD42021281588). We searched multiple databases up to August 2021. We included only randomized trials of patients with renal cell carcinoma that had been completely resected. We included patients with locoregional nodal disease if it was surgically removed, and excluded all cases of metastatic disease. We included all adjuvant systemic therapies that were commenced within 90 days of renal surgery. A network meta-analysis was performed using a frequentist approach. Results A total of 13 studies with 8103 patients were included for analysis. Only pembrolizumab (HR 0.74; 95%CI 0.57 to 0.96) and pazopanib (HR 0.80; 95%CI 0.68 to 0.95) improved disease-free survival compared with observation. These 2 treatments were the 2 highest ranked comparisons with a P-score of 0.87 and 0.80. No agent improved overall survival. All agents increased the risk of severe adverse events compared with observation. Conclusions Pembrolizumab and pazopanib were the only 2 adjuvant agents that improved time to disease recurrence compared with observation, with the former likely being the more efficacious. None of the treatments improved overall survival and almost all increased severe adverse events. Introduction
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.042 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".