1. Computational and Systems Biology
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Bias in the reporting of sex and age in biomedical research on mouse models

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Cite this article as: eLife 2016;5:e13615 doi: 10.7554/eLife.13615

Abstract

In animal-based biomedical research, both the sex and the age of the animals studied affect disease phenotypes by modifying their susceptibility, presentation and response to treatment. The accurate reporting of experimental methods and materials, including the sex and age of animals, is essential so that other researchers can build on the results of such studies. Here we use text mining to study 15,311 research papers in which mice were the focus of the study. We find that the percentage of papers reporting the sex and age of mice has increased over the past two decades: however, only about 50% of the papers published in 2014 reported these two variables. We also compared the quality of reporting in six preclinical research areas and found evidence for different levels of sex-bias in these areas: the strongest male-bias was observed in cardiovascular disease models and the strongest female-bias was found in infectious disease models. These results demonstrate the ability of text mining to contribute to the ongoing debate about the reproducibility of research, and confirm the need to continue efforts to improve the reporting of experimental methods and materials.

Article and author information

Author details

  1. Oscar Flórez-Vargas

    Bio-health Informatics Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  2. Andy Brass

    Bio-health Informatics Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom
    For correspondence
    andy.brass@manchester.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
  3. George Karystianis

    Text Mining Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Michael Bramhall

    Bio-health Informatics Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  5. Robert Stevens

    Bio-health Informatics Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Sheena Cruickshank

    Manchester Immunology Group, Faculty of Life Science, The University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  7. Goran Nenadic

    Text Mining Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.

Reviewing Editor

  1. Chris Mungall

Publication history

  1. Received: December 8, 2015
  2. Accepted: February 23, 2016
  3. Accepted Manuscript published: March 3, 2016 (version 1)
  4. Version of Record published: March 24, 2016 (version 2)

Copyright

© 2016, Flórez-Vargas et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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