Adjuvant immunotherapy in renal cell carcinoma: a systematic review and <scp>meta‐analysis</scp>
Bibliographic record
Abstract
OBJECTIVES: To synthesise available data regarding the disease-free survival (DFS) benefit of adjuvant immune checkpoint inhibitors (ICIs) for patients with renal cell carcinoma (RCC) and evaluate the overall safety profile of ICIs in this setting. MATERIALS AND METHODS: We utilised PubMed, Embase, and relevant conference proceedings to identify phase III randomised controlled trials comparing adjuvant ICIs vs placebo/observation for RCC. The primary outcome of interest was DFS. Variables for subgroup analyses were programmed death-ligand 1 (PD-L1) expression, sarcomatoid features, nephrectomy type, and disease-risk category. Secondary outcomes included Grade ≥3 adverse events (AEs), immune-related AEs, and treatment discontinuation due to AEs. All outcomes were analysed using random-effects models owing to inter-study heterogeneity. RESULTS: = 64%) due to differences in inclusion criteria and interventions. While pooled results across the four studies did not demonstrate a significant benefit in DFS overall (hazard ratio [HR] 0.85, 95% confidence interval [CI] 0.69-1.04) there was significant benefit among patients with positive PD-L1 expression (HR 0.72, 95% CI 0.55-0.94) and sarcomatoid features (HR 0.59, 95% CI 0.38-0.91). CONCLUSION: The evidence base to date regarding ICIs as adjuvant therapy in RCC is mixed - conclusions are limited by considerable heterogeneity between studies. However, pooled analyses suggest that patients with positive PD-L1 expression or sarcomatoid features are most likely to benefit from adjuvant immunotherapy.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.022 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".