Efficacy and Safety of Programmed Death-1/Programmed Death-Ligand 1 Inhibitor for Metastatic Urothelial Carcinoma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: The purpose of this study was to evaluate the efficacy and safety of programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors for the treatment of metastatic urothelial carcinoma (mUC). Methods: A literature search was conducted of PubMed, EMBASE, and the Cochrane Library and was limited to the English literature. Randomized controlled trials (RCTs) published up to July 2022 were considered for inclusion. The outcomes were progression-free survival (PFS), overall survival (OS), objective response rate (ORR), and grade ≥ 3 treatment-related AEs (TRAE). Subgroup analysis was performed based on the PD-L1 expression status, and the differences between first- and second-line PD-1/PD-L1 inhibitors were estimated. Results: We included five RCTs comprising 3584 patients in the analysis. Compared with chemotherapy alone, the use of PD-1/PD-L1 inhibitors as monotherapy did not significantly prolong OS [hazard ratios (HR), 0.90; 95% CI, 0.81–1.00] or PFS (HR, 1.12; 95% CI, 0.95–1.32). However, the PD-1/PD-L1 inhibitor combined with chemotherapy significantly improved both OS (HR, 0.85; 95% CI, 0.74–0.96) and PFS (HR, 0.80; 95% CI, 0.71–0.90). Additionally, subgroup analysis showed that in mUC with PD-L1 expression ≥ 5%, treatment with the PD-1/PD-L1 inhibitor alone did not reduce the risk of death. Safety analysis showed that the PD-1/PD-L1 inhibitor alone did not significantly increase the incidence rates of grade ≥ 3 TRAEs. Conclusions: The results show that use of the PD-1/PD-L1 inhibitor alone as first-line treatment is similar to chemotherapy in terms of both survival and response rates. However, the PD-1/PD-L1 inhibitor plus chemotherapy has a significant benefit in terms of PFS or OS. Nonetheless, more RCTs are warranted to evaluate efficiency and safety in the combination regimen of chemotherapy and PD-1/PD-L1 inhibitors.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".