Conference paper Open Access

Data Extraction and Synthesis in Systematic Reviews of Diagnostic Test Accuracy: A Corpus for Automating and Evaluating the Process

Norman, Christopher; Leeflang, Mariska; Névéol, Aurélie


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  <identifier identifierType="DOI">10.5281/zenodo.2574710</identifier>
  <creators>
    <creator>
      <creatorName>Norman, Christopher</creatorName>
      <givenName>Christopher</givenName>
      <familyName>Norman</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-9306-2363</nameIdentifier>
      <affiliation>LIMSI, CNRS</affiliation>
    </creator>
    <creator>
      <creatorName>Leeflang, Mariska</creatorName>
      <givenName>Mariska</givenName>
      <familyName>Leeflang</familyName>
      <affiliation>AMC, University of Amsterdam</affiliation>
    </creator>
    <creator>
      <creatorName>Névéol, Aurélie</creatorName>
      <givenName>Aurélie</givenName>
      <familyName>Névéol</familyName>
      <affiliation>LIMSI, CNRS</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Data Extraction and Synthesis in Systematic Reviews of Diagnostic Test Accuracy: A Corpus for Automating and Evaluating the Process</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2018</publicationYear>
  <dates>
    <date dateType="Issued">2018-11-05</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/2574710</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.2574709</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/miror</relatedIdentifier>
  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;Background: Systematic reviews are critical for obtaining accurate estimates of diagnostic test accuracy, yet these require extracting information buried in free text articles, which is often laborious. Objective: We create a dataset describing the data extraction and synthesis processes in 63 DTA systematic reviews, and demonstrate its utility by using it to replicate the data synthesis in the original reviews. Method: We construct our dataset using a custom automated extraction pipeline complemented with manual extraction, verification, and post-editing. We evaluate using manual assessment by two annotators and by comparing against data extracted from source files. Results: The constructed dataset contains 5,848 test results for 1,354 diagnostic tests from 1,738 diagnostic studies. We observe an extraction error rate of 0.06&amp;ndash;0.3%. Conclusions: This constitutes the first dataset describing the later stages of the DTA systematic review process, and is intended to be useful for automating or evaluating the process.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/676207/">676207</awardNumber>
      <awardTitle>Methods in Research on Research</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
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