Association between PD-L1 Expression and the Prognosis and Clinicopathologic Features of Renal Cell Carcinoma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The expression of programmed cell death-ligand 1 (PD-L1) and its correlation with the prognosis and clinicopathologic features of renal cell carcinoma (RCC) remain controversial to date. Concerning this issue, we had conducted a meta-analysis of relevant studies searched in the Web of Science, PubMed, EMBASE, and Cochrane Library databases. The Newcastle-Ottawa quality assessment scale was applied to assess the quality of the included studies. The hazard ratio (HR) and its corresponding 95% confidence intervals (CIs) were collected by Stata 12.0 and used for the results of overall survival (OS) and disease-free survival (DFS). A total of 1,644 patients in 8 studies were included in this meta-analysis. Results showed that PD-L1 expression significantly correlated with OS (HR = 1.98, 95% CI: 1.22-3.22, Z = 2.77, p = 0.006) and DFS (HR = 3.70, 95% CI: 2.07-6.62, Z = 4.40, p = 0.0001) in ccRCC. Subgroup analysis indicated that PD-L1 expression significantly correlated with the lymph-gland transfer ratio (HR = 2.45, 95% CI: 1.02-5.92, Z = 1.99, p = 0.05) and tumor necrosis (HR = 6.05, 95% CI: 3.78-9.67, Z = 7.51, p < 0.00001). This meta-analysis suggests that PD-L1 expression is a valuable prognostic tool for patients with ccRCC. Subgroup analyses demonstrated that it was helpful for screening patients with RCC who need anti-PD-1/PD-L1 treatment and support them to benefit from such immune-targeted therapy.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".