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Evaluating data extraction error by a large language model from randomised controlled trials: a large-scale empirical

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Large language models (LLMs) like Claude 3.5 Sonnet show low data extraction error rates from randomized controlled trials (RCTs). However, careful verification of LLM outputs is crucial for evidence synthesis applications.

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Area of Science:

  • Medical informatics
  • Artificial intelligence in healthcare
  • Clinical trial methodology

Background:

  • Large language models (LLMs) offer potential for automating data extraction from clinical research.
  • Evaluating the accuracy of LLMs in extracting data from randomized controlled trials (RCTs) is essential.

Purpose of the Study:

  • To assess the data extraction accuracy of Claude 3.5 Sonnet on RCTs.
  • To identify common error types and influencing factors in LLM-based data extraction.

Main Methods:

  • An empirical study compared Claude 3.5 Sonnet's extractions against a human-verified dataset of 664 RCTs.
  • Data extraction focused on basic trial information and adverse outcomes.
  • Error rates were calculated and analyzed by error type and trial reporting quality (CONSORT adherence).

Main Results:

  • The overall data extraction error rate for Claude 3.5 Sonnet was 6.6%.
  • Misallocation (57.1%) and omitted data (23.2%) were the most frequent error types.
  • Higher adherence to Consolidated Standards of Reporting Trials (CONSORT) guidelines correlated with lower LLM extraction errors.

Conclusions:

  • Claude 3.5 Sonnet demonstrates a relatively low error rate for RCT data extraction.
  • LLM applications in evidence synthesis require rigorous human oversight and detailed checking of outputs.