12,445 papers · continuously updated · last export: 10 Aug 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

  • Future research should extend RPYS by integrating advanced disambiguation algorithms to better merge cited references variants; the study highlights the necessity of combining RPYS with manual variant
  • The authors identify that "existing benchmarks and systems remain predominantly text-centric, with limited evaluation of whether visual elements are factually reliable and well aligned with the surrou
  • The paper identifies the need for continued advancement in intelligent and precision-oriented research in pediatric cardiomyopathy genetics, with emphasis on digital healthcare integration and data-dr
  • Generalizability to extraction tasks requiring construct interpretation, judgment, or classification (beyond numeric variables)
  • Performance with novice or less-experienced coders

Representative papers

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