Evidence map›Paper›PMID 26939790›Full record

ArticleeLife2016

Bias in the reporting of sex and age in biomedical research on mouse models.

Oscar Flórez-Vargas, Andy Brass, George Karystianis, Michael Bramhall, Robert Stevens, Sheena Cruickshank, Goran Nenadic

Abstract read
In one paragraph

Article in eLife, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

66 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Importance of Systematic Reviews and Meta-analyses of Animal Studies: Challenges for Animal-to-Human Translation.Journal of the American Association for Laboratory Animal Science : JAALAS · 2020
    Pooled it
  5. Pooled it
  6. Sex-Biased Immunity: The Hidden Variable in Immuno-oncology.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Toward Sex-Specific Biomaterials Innovation: A Perspective.ACS biomaterials science & engineering · 2025
    Review
  13. Article
  14. Article
  15. Review
  16. Article
  17. Quantitative in Vivo Molecular MRI.Advanced materials (Deerfield Beach, Fla.) · 2024
    Review
  18. Enhanced CaJACC. Clinical electrophysiology · 2024
    Article
  19. Article
  20. Article

6 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Oscar Flórez-VargasBio-health Informatics Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom.
Andy BrassBio-health Informatics Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom.
George KarystianisText Mining Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom.
Michael BramhallBio-health Informatics Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom.ORCID http://orcid.org/0000-0001-5938-158X
Robert StevensBio-health Informatics Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom.
Sheena CruickshankManchester Immunology Group, Faculty of Life Science, The University of Manchester, Manchester, United Kingdom.
Goran NenadicText Mining Group, School of Computer Science, The University of Manchester, Manchester, United Kingdom.

Funding

Medical Research Council MC_PC_13042Medical Research Council MR/K006665/1
6 · The paper itself

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.

Indexed as

Disease Models, AnimalSelection BiasAge DistributionAnimalsBiomedical ResearchData MiningMiceSex Distributionagebiascomputational biologymousereportingsexsystems biologytext-mining

Identifiers

PMID26939790
PMCPMC4821800

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.