title: Citations alone were enough to predict favorable conclusions in reviews of neuraminidase inhibitors.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Centre for Health Informatics, Australian Institute of Health Innovation, The University of New South Wales, Sydney, NSW 2052, Australia.
authors.forename: Xujuan
authors.initials: X
authors.lastname: Zhou
authors.affiliation: Centre for Health Informatics, Australian Institute of Health Innovation, The University of New South Wales, Sydney, NSW 2052, Australia.
authors.forename: Ying
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Centre for Health Informatics, Australian Institute of Health Innovation, The University of New South Wales, Sydney, NSW 2052, Australia.
authors.forename: Guy
authors.initials: G
authors.lastname: Tsafnat
authors.affiliation: Centre for Health Informatics, Australian Institute of Health Innovation, The University of New South Wales, Sydney, NSW 2052, Australia.
authors.forename: Enrico
authors.initials: E
authors.lastname: Coiera
authors.affiliation: Division of Emergency Medicine, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA; Children's Hospital Informatics Program, Boston Children's Hospital, Boston, MA, USA.
authors.forename: Florence T
authors.initials: FT
authors.lastname: Bourgeois
authors.affiliation: Centre for Health Informatics, Australian Institute of Health Innovation, The University of New South Wales, Sydney, NSW 2052, Australia. Electronic address: a.dunn@unsw.edu.au.
authors.forename: Adam G
authors.initials: AG
authors.lastname: Dunn
mesh_terms: D001185:Artificial Intelligence; D004791:Enzyme Inhibitors / Q000592:standards* / Q000627:therapeutic use*; D006801:Humans; D007251:Influenza, Human / Q000188:drug therapy; D009439:Neuraminidase / Q000037:antagonists & inhibitors*; D011642:Publications / Q000706:statistics & numerical data*; D012107:Research Design; D012196:Review Literature as Topic; D015983:Selection Bias
chemical_list: D004791:Enzyme Inhibitors; D009439:Neuraminidase
keywords: Bibliometrics; Citation analysis; Evidence synthesis; Neuraminidase inhibitors; Reviews as a topic; Supervised machine learning