Bias in the reporting of sex and age in biomedical research on mouse models
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 s...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2016-03-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/13615 |
| _version_ | 1811181049288327168 |
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| author | Oscar Flórez-Vargas Andy Brass George Karystianis Michael Bramhall Robert Stevens Sheena Cruickshank Goran Nenadic |
| author_facet | Oscar Flórez-Vargas Andy Brass George Karystianis Michael Bramhall Robert Stevens Sheena Cruickshank Goran Nenadic |
| author_sort | Oscar Flórez-Vargas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T09:13:06Z |
| format | Article |
| id | doaj.art-c0c9acb5f72748899183111e771eac59 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-11T09:13:06Z |
| publishDate | 2016-03-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-c0c9acb5f72748899183111e771eac592022-12-22T04:32:26ZengeLife Sciences Publications LtdeLife2050-084X2016-03-01510.7554/eLife.13615Bias in the reporting of sex and age in biomedical research on mouse modelsOscar Flórez-Vargas0Andy Brass1George Karystianis2Michael Bramhall3https://orcid.org/0000-0001-5938-158XRobert Stevens4Sheena Cruickshank5Goran Nenadic6Bio-health Informatics Group, School of Computer Science, The University of Manchester, Manchester, United KingdomBio-health Informatics Group, School of Computer Science, The University of Manchester, Manchester, United KingdomText Mining Group, School of Computer Science, The University of Manchester, Manchester, United KingdomBio-health Informatics Group, School of Computer Science, The University of Manchester, Manchester, United KingdomBio-health Informatics Group, School of Computer Science, The University of Manchester, Manchester, United KingdomManchester Immunology Group, Faculty of Life Science, The University of Manchester, Manchester, United KingdomText Mining Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom; Manchester Institute of Biotechnology, University of Manchester, Manchester, United KingdomIn 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.https://elifesciences.org/articles/13615text-miningsexagereportingbias |
| spellingShingle | Oscar Flórez-Vargas Andy Brass George Karystianis Michael Bramhall Robert Stevens Sheena Cruickshank Goran Nenadic Bias in the reporting of sex and age in biomedical research on mouse models eLife text-mining sex age reporting bias |
| title | Bias in the reporting of sex and age in biomedical research on mouse models |
| title_full | Bias in the reporting of sex and age in biomedical research on mouse models |
| title_fullStr | Bias in the reporting of sex and age in biomedical research on mouse models |
| title_full_unstemmed | Bias in the reporting of sex and age in biomedical research on mouse models |
| title_short | Bias in the reporting of sex and age in biomedical research on mouse models |
| title_sort | bias in the reporting of sex and age in biomedical research on mouse models |
| topic | text-mining sex age reporting bias |
| url | https://elifesciences.org/articles/13615 |
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