Adjuvant immunotherapy in renal cell carcinoma: A systematic review and meta-analysis of randomized clinical trials.
Bibliographic record
Abstract
671 Background: There has been interest in adjuvant immune checkpoint inhibition (ICI) following surgical resection in patients with high-risk renal cell carcinoma (RCC) given high recurrence rates and approvals of ICI in metastatic RCC. The primary objective of this analysis was to synthesize available data regarding the disease-free survival (DFS) benefit of adjuvant ICIs for patients with RCC. Methods: This systematic review was performed according to the PRISMA guidelines. The protocol was registered in PROSPERO (CRD42022361599). We searched PubMed, EMBASE, and relevant conference proceedings to identify phase III randomized controlled trials (RCTs) comparing adjuvant ICI versus placebo/observation. The primary outcome of interest was DFS. Results: Among the four included studies, one demonstrated a significant DFS benefit. There was considerable clinical and statistical heterogeneity (I2=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 (HR 0.85, 95% 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) or sarcomatoid features (HR 0.59, 95% CI 0.38-0.91). Conclusions: The evidence base to date regarding ICI as adjuvant therapy in RCC is mixed – conclusions are limited by considerable heterogeneity between studies. However, pooled analyses suggest that patients with positive PDL1 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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".