SynthesisFrontiers in oncology2026
Cohort-level proportions of low-grade glioma (WHO grades I-II) in mixed glioma cohorts: a single-arm meta-analysis for epidemiological benchmarking and clinical trial design.
Synthesis in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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8 authors.
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Abstract
Background: Mixed-grade glioma surgical cohorts exhibit highly variable low-grade glioma (LGG) proportions across studies. This meta-analysis quantifies pooled LGG proportions to provide cohort-level epidemiological benchmarks for researchers, with applications in clinical trial design, health economics, and institutional benchmarking. Methods: A single-arm meta-analysis of 191 cohort studies (25,847 patients) with Grade II numerators and denominators from Embase, MEDLINE, PubMed, and Scopus (PRISMA 2020) used Freeman-Tukey random-effects synthesis with Cochrane risk-of-bias assessments; subgroup analyses examined study design, geography, era, molecular specificity, and interventions; sensitivity analyses tested robustness. Results: The pooled LGG proportion was 57% (95% CI: 52%-61%; I²=99%; n=191). Mixed LGG/high-grade gliomas (HGG) cohorts showed 41% (32%-50%; 45 studies, 6,847 patients) versus 82% (77%-88%) in pure WHO Grade II cohorts (25 studies). Surgery-only cohorts demonstrated 46% (33%-59%) versus 86% (74%-98%) with targeted therapies, and 93% (90%-96%) in randomized controlled trials. Sensitivity analyses confirmed stability (50%-62%). Conclusion: In mixed-grade glioma cohorts, an LGG proportion approaching 60% may serve as a cohort-level epidemiological reference for understanding the historical grade composition of mixed surgical series. This meta-analysis primary value lies in applications such as clinical trial design, health economics and epidemiological modeling, and institutional benchmarking. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD420261323907.
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