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Can animal data translate to innovations necessary for a new era of patient-centred and individualised healthcare? Bias in preclinical animal research

Overview of attention for article published in BMC Medical Ethics, July 2015
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3 Facebook pages

Citations

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35 Dimensions

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123 Mendeley
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Title
Can animal data translate to innovations necessary for a new era of patient-centred and individualised healthcare? Bias in preclinical animal research
Published in
BMC Medical Ethics, July 2015
DOI 10.1186/s12910-015-0043-7
Pubmed ID
Authors

Susan Bridgwood Green

Abstract

The public and healthcare workers have a high expectation of animal research which they perceive as necessary to predict the safety and efficacy of drugs before testing in clinical trials. However, the expectation is not always realised and there is evidence that the research often fails to stand up to scientific scrutiny and its 'predictive value' is either weak or absent. Problems with the use of animals as models of humans arise from a variety of biases and systemic failures including: 1) bias and poor practice in research methodology and data analysis; 2) lack of transparency in scientific assessment and regulation of the research; 3) long-term denial of weaknesses in cross-species translation; 4) profit-driven motives overriding patient interests; 5) lack of accountability of expenditure on animal research; 6) reductionist-materialism in science which tends to dictate scientific inquiry and control the direction of funding in biomedical research. Bias in animal research needs to be addressed before medical research and healthcare decision-making can be more evidence-based. Research funding may be misdirected on studying 'disease mechanisms' in animals that cannot be replicated outside tightly controlled laboratory conditions, and without sufficient critical evaluation animal research may divert attention away from avenues of research that hold promise for human health. The potential for harm to patients and trial volunteers from reliance on biased animal data(1) requires measures to improve its conduct, regulation and analysis. This article draws attention to a few of the many forms of bias in animal research that have come to light in the last decade and offers a strategy incorporating ten recommendations stated at the end of each section on bias. The proposals need development through open debate and subsequent rigorous implementation so that reviewers may determine the value of animal research to human health. The 10Rs + are protected by a Creative Commons Attribution 3.0 Unported License and therefore may be 'shared, remixed or built on, even commercially, so long as attributed by giving appropriate credit with a link to the license, and indicate if changes were made.'

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 123 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Unknown 122 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 19 15%
Researcher 18 15%
Student > Ph. D. Student 18 15%
Student > Bachelor 14 11%
Professor > Associate Professor 10 8%
Other 23 19%
Unknown 21 17%
Readers by discipline Count As %
Medicine and Dentistry 26 21%
Agricultural and Biological Sciences 12 10%
Neuroscience 8 7%
Nursing and Health Professions 8 7%
Pharmacology, Toxicology and Pharmaceutical Science 7 6%
Other 35 28%
Unknown 27 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 03 May 2018.
All research outputs
#7,474,413
of 25,847,449 outputs
Outputs from BMC Medical Ethics
#639
of 1,120 outputs
Outputs of similar age
#79,365
of 276,036 outputs
Outputs of similar age from BMC Medical Ethics
#9
of 16 outputs
Altmetric has tracked 25,847,449 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 1,120 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.8. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 276,036 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.