Bias in the reporting of sex and age in biomedical research on mouse models
Open accessAbstract
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.
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Evidence shows that the majority of publications fail to include key information and there is significant scope to improve the reporting of studies involving animal research.
“However, evidence shows that the majority of publications fail to include key information and there is significant scope to improve the reporting of studies involving animal research [1–4].”
From Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0 (2020) · cites this paper as [2]
The Results (‘Reporting of sex and age’; page not available) say that only about 50% of mouse-model papers published since 2012 reported both variables. The Discussion identifies this as a reporting problem, but the study does not establish that a majority of animal-research publications generally omit key information. (LLM, conf=0.86)
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Research works printed but not linked: 41
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- Authors
- Flórez-Vargas, O., Brass, A., Karystianis, G., Bramhall, M., Stevens, R., Cruickshank, S., & Nenadic, G.
- Affiliations
- University of Manchester (GB)
- Text Mining Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom
- Corresponding author
- Flórez-Vargas, Oscar
- Brass, Andy
- ORCID
- Venue
- eLife
- Publisher
- eLife Sciences Publications, Ltd
- Published
- 2016-03-03
- Volume
- 5
- ISSN
- 2050-084X (ISSN-L)
- PMC
- PMC4821800
- OpenAlex
- W2291538400
- Cited by
- 124
- References
- 57
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 18 · 2023: 16 · 2022: 6 · 2021: 14 · 2020: 10 · 2019: 12 · 2018: 12 · 2017: 12 · 2016: 8
- FWCI
- 14.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- eLife (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (acceptedVersion, other-oa)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Departamento Administrativo de Ciencia, Tecnología e Innovación (Scholarship)
- Engineering and Physical Sciences Research Council (Scholarship)
- Epistem Ltd (Scholarship)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Sex and Gender in Healthcare
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- sex bias, age reporting, mouse model, animal age, reporting quality, reproducibility, preclinical research, cardiovascular disease models, epidemic modeling
- MeSH terms
- Animals, Disease Models, Animal, Selection Bias, Age Distribution, Sex Distribution, Biomedical Research, Mice, Data Mining
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