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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        <foaf:name>Norman, Christopher</foaf:name>
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        <foaf:name>Leeflang, Mariska</foaf:name>
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        <foaf:name>Névéol, Aurélie</foaf:name>
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        <foaf:familyName>Névéol</foaf:familyName>
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    <dct:title>Data Extraction and Synthesis in Systematic Reviews of Diagnostic Test Accuracy: A Corpus for Automating and Evaluating the Process</dct:title>
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        <dct:identifier rdf:datatype="http://www.w3.org/2001/XMLSchema#string">10.13039/501100000780</dct:identifier>
        <foaf:name>European Commission</foaf:name>
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    <dct:description>&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;</dct:description>
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    <dct:title>Methods in Research on Research</dct:title>
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