Association of PD-L1 Expression with Clinicopathologic Charactersin Gastric Cancer: A Comprehensive Meta-analysis
Bibliographic record
Abstract
PURPOSE: The expression level of programmed death ligand-1(PD-L1) in patients with gastric cancer is the key to determining the use of immune drugs. The relationship between PD-L1 expression level and clinical characteristics is worth exploring. METHODS: By setting the search terms correlated to PD-L1 and gastric cancer, a nearly comprehensive search was carried out in four major databases, and the deadline for searching was September 1, 2022. The retrieved documents were further screened by strict inclusion and exclusion criteria after removing the duplication. Next, the quality of the included studies was evaluated with the Newcastle-Ottawa Scale (NOS) scale. Finally, the STATA15.1 software was used to process data and draw plots, and the odds ratios (ORs) were adopted to assess the pooled effect size. RESULTS: A total of 85 works of literature were included in this study through screening strictly, and detailed data were extracted after evaluating the quality of the literature. The process of analysis was conducted in the whole population, Asia-Africa population, European and American population, and Asian population with CPS≥1, amd all found that the expression of PD-L1 in gastric cancer was correlated with age, tumor size, EBV infection, Her-2 expression and microsatellite status. However, the subgroup of the region also found some differences in Asian and Western regions, which was interesting and worth studying further. The included research of this study did not have significant publish bias. CONCLUSION: After careful analysis, this study found that age (>60 years), tumor size (>5cm), EBV infection (+), Her-2 expression (+), microsatellite status (MSI), and mismatch repair status (dMMR) were risk factors for positive expression of PD-L1 in gastric cancer.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.036 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 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".