An open competition involving thousands of competitors failed to construct useful abstract classifiers for new diagnostic test accuracy systematic reviews.

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    • Source:
      Publisher: Wiley Blackwell Country of Publication: England NLM ID: 101543738 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1759-2887 (Electronic) Linking ISSN: 17592879 NLM ISO Abbreviation: Res Synth Methods Subsets: MEDLINE
    • Publication Information:
      Publication: : Chichester : Wiley Blackwell
      Original Publication: Malden, MA : John Wiley & Sons, 2010-
    • Subject Terms:
    • Abstract:
      There are currently no abstract classifiers, which can be used for new diagnostic test accuracy (DTA) systematic reviews to select primary DTA study abstracts from database searches. Our goal was to develop machine-learning-based abstract classifiers for new DTA systematic reviews through an open competition. We prepared a dataset of abstracts obtained through database searches from 11 reviews in different clinical areas. As the reference standard, we used the abstract lists that required manual full-text review. We randomly splitted the datasets into a train set, a public test set, and a private test set. Competition participants used the training set to develop classifiers and validated their classifiers using the public test set. The classifiers were refined based on the performance of the public test set. They could submit as many times as they wanted during the competition. Finally, we used the private test set to rank the submitted classifiers. To reduce false exclusions, we used the Fbeta measure with a beta set to seven for evaluating classifiers. After the competition, we conducted the external validation using a dataset from a cardiology DTA review. We received 13,774 submissions from 1429 teams or persons over 4 months. The top-honored classifier achieved a Fbeta score of 0.4036 and a recall of 0.2352 in the external validation. In conclusion, we were unable to develop an abstract classifier with sufficient recall for immediate application to new DTA systematic reviews. Further studies are needed to update and validate classifiers with datasets from other clinical areas.
      (© 2023 John Wiley & Sons, Ltd.)
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    • Grant Information:
      Fujifilm Corporation
    • Contributed Indexing:
      Keywords: diagnostic test accuracy; machine learning; open competition; search filter; systematic review
    • Publication Date:
      Date Created: 20230620 Date Completed: 20230915 Latest Revision: 20230915
    • Publication Date:
      20240105
    • Accession Number:
      10.1002/jrsm.1649
    • Accession Number:
      37337729