Citations alone were enough to predict favorable conclusions in reviews of neuraminidase inhibitors

Zhou, Xujuan and Wang, Ying and Tsafnat, Guy and Coiera, Enrico and Bourgeois, Florence T. and Dunn, Adam G. (2015) Citations alone were enough to predict favorable conclusions in reviews of neuraminidase inhibitors. Journal of Clinical Epidemiology, 68 (1). pp. 87-93. ISSN 0895-4356

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.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty / Department / School: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
Date Deposited: 29 Aug 2016 01:09
Last Modified: 13 Jan 2017 01:50
Uncontrolled Keywords: Neuraminidase inhibitors; Bibliometrics; Evidence synthesis; Reviews as a topic; Citation analysis; Supervised machine learning
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
11 Medical and Health Sciences > 1117 Public Health and Health Services > 111706 Epidemiology
Socio-Economic Objective: C Society > 92 Health > 9204 Public Health (excl. Specific Population Health) > 920499 Public Health (excl. Specific Population Health) not elsewhere classified
Identification Number or DOI: 10.1016/j.jclinepi.2014.09.014
URI: http://eprints.usq.edu.au/id/eprint/29641

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