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Using Deep Learning to Detect Spinal Cord Diseases on Thoracolumbar Magnetic Resonance Images of Dogs

Overview of attention for article published in Frontiers in Veterinary Science, November 2021
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

twitter
8 X users

Citations

dimensions_citation
18 Dimensions

Readers on

mendeley
50 Mendeley
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Title
Using Deep Learning to Detect Spinal Cord Diseases on Thoracolumbar Magnetic Resonance Images of Dogs
Published in
Frontiers in Veterinary Science, November 2021
DOI 10.3389/fvets.2021.721167
Pubmed ID
Authors

Anika Biercher, Sebastian Meller, Jakob Wendt, Norman Caspari, Johannes Schmidt-Mosig, Steven De Decker, Holger Andreas Volk

Timeline

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X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Other 13 26%
Unspecified 5 10%
Student > Master 4 8%
Student > Doctoral Student 3 6%
Lecturer > Senior Lecturer 2 4%
Other 6 12%
Unknown 17 34%
Readers by discipline Count As %
Veterinary Science and Veterinary Medicine 22 44%
Unspecified 5 10%
Medicine and Dentistry 2 4%
Social Sciences 1 2%
Neuroscience 1 2%
Other 1 2%
Unknown 18 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 24 November 2021.
All research outputs
#7,656,930
of 23,310,485 outputs
Outputs from Frontiers in Veterinary Science
#1,458
of 6,529 outputs
Outputs of similar age
#154,722
of 440,489 outputs
Outputs of similar age from Frontiers in Veterinary Science
#75
of 465 outputs
Altmetric has tracked 23,310,485 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,529 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has done well, scoring higher than 76% of its peers.
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 440,489 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 62% of its contemporaries.
We're also able to compare this research output to 465 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.