A Systematic Review and Meta-analysis of PD-1 and PD-L1 Inhibitors Monotherapy in Metastatic Gastric and Gastroesophageal Junction Adenocarcinoma
Bibliographic record
Abstract
Immune checkpoint inhibitors are new targeted treatments that harness the body's immune system to attack cancers.Drugs that are most extensively used among checkpoint inhibitors inhibit the PD-L1 or PD-1 (programmed death 1) ligand or receptor pair and are currently approved for many cancer indications.In gastric or gastroesophageal junction adenocarcinomas one inhibitor, pembrolizumab has regulatory approval for PD-L1 positive carcinomas.This meta-analysis investigates available data on the efficacy of PD-L1 or PD-1 inhibitors as a class in gastric or gastroesophageal junction adenocarcinomas.The literature was reviewed to identify clinical studies that included arms with PD-L1 or PD-1 inhibitors as monotherapy in gastric or gastroesophageal junction adenocarcinomas.Relevant patient characteristics, outcomes, and adverse effects were recorded.Summary estimates of response rates (RR) and survival were calculated using a random or fixed effect model, depending on heterogeneity.Six studies with a total of 1068 patients were included in the analysis.The summary RR was 10.63% (95% confidence interval (CI) 5.36-15.89%).The summary disease control rate (DCR) was 28.11% (95% CI 24.60-31.63%).Summary progression-free survival (PFS) was 1.59 months (95% CI 1.24-1.94months).Summary overall survival (OS) was 5.72 months (95% CI 0-12.19 months).A subset of patients derived long-term benefits as seen in other cancer locations.The adverse effect rate was low and consistent with that in other disease locations.Low efficacy of immune checkpoint inhibitors as a class in gastric or gastroesophageal junction adenocarcinomas is observed in this analysis and stresses the need for effective biomarker use for the identification of most probable responders.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.007 | 0.009 |
| 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) | 0.004 | 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".