Abstract
Objectives
To examine the use of supervised machine learning to identify biases in evidence selection
and determine if citation information can predict favorable conclusions in reviews
about neuraminidase inhibitors.
Study Design and Setting
Reviews of neuraminidase inhibitors published during January 2005 to May 2013 were
identified by searching PubMed. In a blinded evaluation, the reviews were classified
as favorable if investigators agreed that they supported the use of neuraminidase
inhibitors for prophylaxis or treatment of influenza. Reference lists were used to
identify all unique citations to primary articles. Three classification methods were
tested for their ability to predict favorable conclusions using only citation information.
Results
Citations to 4,574 articles were identified in 152 reviews of neuraminidase inhibitors,
and 93 (61%) of these reviews were graded as favorable. Primary articles describing
drug resistance were among the citations that were underrepresented in favorable reviews.
The most accurate classifier predicted favorable conclusions with 96.2% accuracy,
using citations to only 24 of 4,574 articles.
Conclusion
Favorable conclusions in reviews about neuraminidase inhibitors can be predicted using
only information about the articles they cite. The approach highlights how evidence
exclusion shapes conclusions in reviews and provides a method to evaluate citation
practices in a corpus of reviews.
Keywords
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Article info
Publication history
Published online: October 21, 2014
Accepted:
September 12,
2014
Footnotes
Funding: This study was supported by National Health and Medical Research Council Program Grant 568612 and Project Grant 1045065. The sponsors had no role in the study.
Conflict of interest: None.
Identification
Copyright
© 2015 Elsevier Inc. Published by Elsevier Inc. All rights reserved.
