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Single data extraction generated more errors than double data extraction in systematic reviews.

Nina Buscemi1, Lisa Hartling, Ben Vandermeer

  • 1Department of Pediatrics, University of Alberta/Capital Health Evidence-Based Practice Centre, Edmonton, Alberta T6G 2J3, Canada. nina.buscemi@ualberta.ca

Journal of Clinical Epidemiology
|June 13, 2006
PubMed
Summary
This summary is machine-generated.

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Single data extraction in systematic reviews leads to more errors but is faster than double data extraction. Effect estimates were similar, but increased errors warrant caution.

Area of Science:

  • Medical Informatics
  • Evidence Synthesis
  • Systematic Review Methodology

Background:

  • Systematic reviews rely on accurate data extraction.
  • The potential for errors in single vs. double data extraction is a critical concern.

Purpose of the Study:

  • To compare error frequency between single and double data extraction.
  • To evaluate differences in treatment effect estimates from both methods.
  • To assess the time efficiency of single vs. double data extraction.

Main Methods:

  • A pilot study randomized reviewers to data extraction or verification roles.
  • McNemar test compared error frequencies; paired t-test compared time.
  • Standard meta-analytic techniques calculated efficacy estimates.

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Main Results:

  • Single data extraction had significantly more errors (21.7% relative difference, P=.019).
  • Effect estimates showed no substantial differences between methods for most outcomes.
  • Single data extraction was faster by 36.1% (P=.003).

Conclusions:

  • Single data extraction is associated with a higher error rate.
  • Reviewers and readers should consider potential impacts of errors on effect estimates in systematic reviews.