Efficacy and safety of pembrolizumab in patients with advanced endometrial cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective This meta-analysis evaluated pembrolizumab monotherapy and combination therapy’s efficacy and safety in recurrent or advanced endometrial cancer (EC). Methods We utilized PubMed, Embase, Cochrane Library, and Web of Science databases to identify clinical trials that were used to search literature from July 2013 to July 2023 to evaluate the efficacy and safety of pembrolizumab in patients with advanced EC. Eight studies with 2,742 patients were included. Outcomes were progression-free survival (PFS), overall survival (OS), objective response rate (ORR), complete remission (CR), and adverse events (AEs); a subgroup analysis was carried out based on combination treatment regimens. Quality assessment of the included studies was conducted using the Cochrane Risk of Bias Tool, the Newcastle–Ottawa Scale (NOS), and the Joanna Briggs Institute (JBI) critical appraisal checklist. Results Pembrolizumab reduced progression risk [hazard ratio (HR): 0.53; 95% confidence interval (CI): 0.44, 0.63; p < 0.00001] and death risk when combined with lenvatinib (HR: 0.67; 95% CI: 0.59, 0.76; p < 0.00001). Pembrolizumab monotherapy and lenvatinib combination achieved higher ORR (OR: 3.61; 95% CI: 2.12, 6.13; p < 0.00001) and CR rates (OR: 2.7; 95% CI: 1.59, 4.57; p < 0.05) than controls. Single-arm studies: 8% CR and 4% PR in pembrolizumab-treated patients. Pooled AE incidence: 86%, with 43% grade 3/4. Two randomized controlled trials (RCTs) found that the pembrolizumab group had a higher incidence of grade 3 or 4 AEs compared to the control group (OR: 2.23; 95% CI: 1.23, 4.04; p = 0.008). Conclusion Pembrolizumab monotherapy or combination significantly improves survival in recurrent or advanced EC and has manageable toxicity albeit with a relatively high incidence of treatment-related AEs.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.051 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".