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Source note2 min read

AutoSynthesis: An agentic system for automated meta-analysis

Source

arXiv

arxiv.org/abs/2607.15247v1

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Source

Plain-Language Brief

AutoSynthesis is a multi-agent system designed for automated meta-analysis of research questions in natural language. The system searches for scientific literature, assesses eligibility, extracts quantitative statistics, computes standardized effect sizes, and performs random-effects meta-analysis. It also supports heterogeneity analysis and risk-of-bias assessment. The results produced by AutoSynthesis are comparable to those of manual evidence syntheses.

What the Document Says

  • AutoSynthesis is a system for automated meta-analysis
  • It processes research questions in natural language
  • It formulates search strategies, retrieves scientific literature, screens studies, assesses eligibility, extracts quantitative statistics, computes standardized effect sizes, and performs random-effects meta-analysis
  • AutoSynthesis supports heterogeneity analysis and risk-of-bias assessment
  • It produces a transparent report aligned with PRISMA guidelines
  • In the application, it screened over 28 studies and extracted more than 20 quantitative claims
  • The pooled effect estimates produced by AutoSynthesis are similar to Hedges' $g$ of expert-conducted meta-analyses
  • AutoSynthesis supports evidence-based decision-making across disciplines

Key Concepts Mentioned

  • AutoSynthesis
  • meta-analysis
  • quantitative statistics
  • standardized effect sizes
  • PRISMA guidelines

Claims Made by the Document

  • AutoSynthesis can make quantitative evidence synthesis more scalable
  • The pooled effect estimates produced by AutoSynthesis are similar to Hedges' $g$ of expert-conducted meta-analyses

Evidence Used

  • It produces pooled effect estimates similar to expert-conducted meta-analyses

Methods / Approach

  • End-to-end multi-agent system
  • Natural language processing
  • Search strategies
  • Literature retrieval
  • Study screening
  • Full-text eligibility assessment
  • Quantitative statistics extraction
  • Standardized effect size computation
  • Random-effects meta-analysis
  • Heterogeneity analysis
  • Risk-of-bias assessment

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The provided text does not mention any limitations of the authors. However, as an assistant, I would like to note potential limitations in the generalization of the results, the quality of the literature retrieved, and the accuracy of the natural language processing.

Open Questions

  • What is the accuracy of AutoSynthesis in various research domains?
  • How does the performance of AutoSynthesis compare to human meta-analysts in terms of precision, recall, and F1 score?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The text suggests that AutoSynthesis has been tested on over 28 studies but does not provide details about the domain(s) or specific research questions involved.
  • It's unclear from the provided text how the comparison between AutoSynthesis and expert-conducted meta-analyses was performed.

Raw Abstract / Excerpt

Evidence synthesis is crucial for turning primary research into reliable knowledge for science, medicine, education, and policy. Yet, quantitative evidence synthesis remains largely manual and difficult to scale. Here, we introduce AutoSynthesis, an end-to-end multi-agent system for automated meta-analysis. Given a research question in natural language, AutoSynthesis formulates a search strategy, retrieves scientific literature, screens candidate studies, assesses full-text eligibility, extracts quantitative statistics, computes standardized effect sizes, and finally performs random-effects meta-analysis. AutoSynthesis further supports heterogeneity analysis to examine how effect sizes vary across moderators, as well as risk-of-bias assessment. As output, AutoSynthesis produces a transparent report aligned with PRISMA guidelines. In our application, AutoSynthesis screened over 28 studies and extracted more than 20 quantitative claims. The pooled effect estimates produced by AutoSynthesis are similar to Hedges' $g$ of expert-conducted meta-analyses, indicating close agreement with manual evidence synthesis. Together, these results show that AutoSynthesis can make quantitative evidence synthesis more scalable, thereby supporting evidence-based decision-making across disciplines.