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Large language models (LLMs) show promise for automating meta-analyses by extracting data from randomized controlled trials (RCTs). While effective for simple outcomes, LLMs struggle with complex data requiring inference.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Clinical Trials

Background:

  • Meta-analyses are crucial for robust treatment effectiveness estimates, synthesizing findings from multiple randomized controlled trials (RCTs).
  • Current meta-analysis requires laborious manual data extraction from individual trial reports, limiting efficiency and scalability.
  • Automating this data extraction using language technologies could enable on-demand meta-analyses.

Purpose of the Study:

  • To evaluate the capability of modern large language models (LLMs) in reliably extracting numerical findings from clinical trial reports for meta-analysis.
  • To assess LLM performance in zero-shot conditional extraction of numerical results linked to interventions, comparators, and outcomes.

Main Methods:

  • Development and release of a granular evaluation dataset of clinical trial reports with annotated numerical findings.
  • Evaluation of seven large language models (LLMs) using a zero-shot approach on the annotated dataset.
  • Focus on extracting numerical results for interventions, comparators, and outcomes from trial reports.

Main Results:

  • Massive LLMs demonstrate near-capability for fully automatic meta-analysis, particularly for dichotomous outcomes like mortality.
  • LLMs exhibit poor performance when outcome measures are complex and require inferential reasoning to tally results.
  • Performance limitations persist even for LLMs trained on biomedical texts.

Conclusions:

  • Large language models (LLMs) are approaching the goal of fully automatic meta-analysis of randomized controlled trials (RCTs).
  • Current LLMs face significant limitations in extracting and synthesizing complex numerical data from trial reports.
  • Further advancements in LLMs are needed to overcome challenges in inferential data processing for comprehensive meta-analysis.