Immunotherapy Combined with Chemotherapy in the First-Line Treatment of Advanced Gastric Cancer: Systematic Review and Bayesian Network Meta-Analysis Based on Specific PD-L1 CPS
Bibliographic record
Abstract
Objective: To compare the efficacy and safety of immunotherapy combined with chemotherapy as the first-line treatment for advanced gastric cancer. Data Sources: Phase III randomised controlled trials were searched from PubMed, Embase, Web of Science, Cochrane Library, and ClinicalTrials databases, and several international conference databases, from inception to 15 November 2024. Results: A total of eight eligible trials involved 7898 patients and eight treatments. The network meta-analysis showed that cadonilimab plus chemotherapy was the most superior treatment in improving overall survival (versus conventional chemotherapy, hazard ratio 0.62, 95% credible interval 0.50 to 0.78) and progression-free survival (0.53, 0.43 to 0.65), and consistency of results were observed in specific PD-L1 combined positive score groups. All immune checkpoint inhibitors combined with chemotherapy improved patient prognosis, but nivolumab plus chemotherapy may lead to an increase in grade 3 or higher adverse events (odds ratio 1.68, 95% credible interval 1.04 to 2.54), and the toxicity of cadonilimab plus chemotherapy was more likely to force patients to discontinue treatment. Conclusions: These results showed that cadonilimab plus chemotherapy had the best overall survival and progression-free survival benefits for advanced gastric cancer patients with HER-2 negative, and was preferentially recommended to patients with positive PD-L1 CPS.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".