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Combined CatWalk Index: an improved method to measure mouse motor function using the automated gait analysis system

Overview of attention for article published in BMC Research Notes, April 2018
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Title
Combined CatWalk Index: an improved method to measure mouse motor function using the automated gait analysis system
Published in
BMC Research Notes, April 2018
DOI 10.1186/s13104-018-3374-x
Pubmed ID
Authors

Samuel T. Crowley, Kazunori Kataoka, Keiji Itaka

Abstract

Measuring motor function in mice is important for studying models of spinal cord injury (SCI) or other diseases. Several methods exist based on visual observation of mice moving in an open field. Though these methods require very little equipment, observers must be trained, and the possibility of human error or subjectivity cannot be eliminated. The Noldus CatWalk XT Automated Gait Analysis system assesses mouse motor function by taking high-resolution videos of the mice, with specialized software to measure several aspects of the animal's gait. This instrument reduces the possibility of human error, but it is not always clear what data is important for assessing motor function. This study used data collected during mouse SCI experiments to create a simple mathematical model that combines the data collected by the CatWalk system into a single score, the Combined CatWalk Index or CCI. The CCI system produces similar results to the Basso Mouse Scale or the CatWalk's Step Sequence Regularity Index. However, the CCI has a significantly smaller coefficient of variation than either other method. Additionally, CCI scoring shows slightly better correlation with impact force. The CCI system is likely to be a useful tool for SCI research.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 18%
Researcher 7 14%
Student > Bachelor 5 10%
Student > Doctoral Student 5 10%
Student > Master 5 10%
Other 6 12%
Unknown 12 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 22%
Neuroscience 9 18%
Medicine and Dentistry 5 10%
Agricultural and Biological Sciences 3 6%
Materials Science 2 4%
Other 5 10%
Unknown 14 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 02 May 2018.
All research outputs
#18,616,159
of 23,881,329 outputs
Outputs from BMC Research Notes
#2,884
of 4,300 outputs
Outputs of similar age
#241,537
of 328,593 outputs
Outputs of similar age from BMC Research Notes
#60
of 101 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,300 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.9. This one is in the 28th percentile – i.e., 28% 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 328,593 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 101 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.