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‘Journal Bias’ in peer-reviewed literature: an analysis of the surgical high-grade glioma literature
  1. Brian R Hirshman1,2,3,
  2. Laurie A Jones3,
  3. Jessica A Tang1,
  4. James A Proudfoot4,
  5. Kathleen M Carley2,3,
  6. Bob S Carter1,5,
  7. Clark C Chen1,5
  1. 1Division of Neurosurgery, Center for Translational and Applied Neuro-Oncology, University of California, San Diego, California, USA
  2. 2Center for Computational Analysis of Social and Organizational Systems, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
  3. 3Computation, Organizations & Society Program, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
  4. 4Clinical and Translational Research Institute, University of California San Diego, San Diego, California, USA
  5. 5Department of Neurosurgery, University of California, San Diego, California, USA
  1. Correspondence to Dr Clark C Chen, Health Science Drive #0987, La Jolla 92093-0987, CA, USA; clarkchen{at}ucsd.edu

Abstract

The core premise of evidence-based medicine is that clinical decisions are informed by the peer-reviewed literature. To extract meaningful conclusions from this literature, one must first understand the various forms of biases inherent within the process of peer review. We performed an exhaustive search that identified articles exploring the question of whether survival benefit was associated with maximal high-grade glioma (HGG) resection and analysed this literature for patterns of publication. We found that the distribution of these 108 articles among the 26 journals to be non-random (p<0.01), with 75 of the 108 published articles (69%) appearing in 6 of the 26 journals (25%). Moreover, certain journals were likely to publish a large number of articles from the same medical academic genealogy (authors with shared training history and/or mentor). We term the tendency of certain types of articles to be published in select journals ‘journal bias’ and discuss the implication of this form of bias as it pertains to evidence-based medicine.

  • META-ANALYSIS
  • TUMOURS
  • NEUROSURGERY
  • NEUROONCOLOGY

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