12,637 papers · updated 18 Sept 2026livingmeta.ai
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Research theme

Meta-Analysis Automation

The Meta-Analysis Automation theme comprises 109 papers in this corpus published between 2004 and 2026. Work here is dominated by Meta-Analysis, Design Science, Benchmarking. 5 open research gaps have been surfaced in this area.

Methodology profile

  • Meta-Analysis27 (25%)
  • Design Science19 (17%)
  • Benchmarking14 (13%)
  • Literature Review14 (13%)
  • Empirical Study13 (12%)
  • Conceptual6 (6%)

Research domains

  • Knowledge Synthesis93 (85%)
  • Data Analysis7 (6%)
  • Scholarly Infrastructure3 (3%)
  • Research Productivity3 (3%)
  • Research Integrity3 (3%)

Frequent sub-topics

evidence synthesis methods for child health using network meta-analysis and individual participant data · 1LLM-assisted meta-research and evidence synthesis for medical questions · 1bibliometric analysis tools and methods · 1deep learning for stroke imaging segmentation and triage · 1deep learning for credit risk prediction in fintech · 1bibliometric and science mapping of AI in agriculture · 1bibliometric mapping and thematic evolution in plant ecology research · 1Bibliometric analysis of microbiome and cancer immunotherapy literature · 1

Open research gaps

  • The authors identify that existing reviews provide broad conceptual perspectives but "few offer a grading-specific, taxonomy-driven analytical framework." They note that hybrid and human-supervised co
  • The abstract does not explicitly identify future research gaps or directions beyond suggesting continued evolution of webometrics and decline of information retrieval systems specialty.
  • The authors identify that "while platforms like Google Scholar and Semantic Scholar track citations for academic papers, no comparable infrastructure exists for monitoring dataset usage in research li
  • Future research should incorporate PubMed, regional databases, and multilingual literature; conduct sensitivity analysis and introduce indicators reflecting the body of disciplines; implement internal
  • The authors identify that "Training large language models for complex reasoning is bottlenecked by the scarcity of verifiable, high-quality data" and that "standard text augmentation often introduces

Representative papers

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