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Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification

Overview of attention for article published in Journal of Cardiovascular Magnetic Resonance (Taylor & Francis Ltd), January 2019
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • Good Attention Score compared to outputs of the same age and source (77th percentile)

Mentioned by

twitter
29 tweeters

Citations

dimensions_citation
46 Dimensions

Readers on

mendeley
114 Mendeley
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Title
Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification
Published in
Journal of Cardiovascular Magnetic Resonance (Taylor & Francis Ltd), January 2019
DOI 10.1186/s12968-018-0509-0
Pubmed ID
Authors

Alex Bratt, Jiwon Kim, Meridith Pollie, Ashley N. Beecy, Nathan H. Tehrani, Noel Codella, Rocio Perez-Johnston, Maria Chiara Palumbo, Javid Alakbarli, Wayne Colizza, Ian R. Drexler, Clerio F. Azevedo, Raymond J. Kim, Richard B. Devereux, Jonathan W. Weinsaft

Twitter Demographics

The data shown below were collected from the profiles of 29 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 114 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 114 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 16%
Researcher 18 16%
Student > Master 16 14%
Student > Bachelor 12 11%
Other 9 8%
Other 17 15%
Unknown 24 21%
Readers by discipline Count As %
Medicine and Dentistry 32 28%
Engineering 27 24%
Computer Science 9 8%
Unspecified 2 2%
Business, Management and Accounting 1 <1%
Other 8 7%
Unknown 35 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 21 December 2019.
All research outputs
#1,904,500
of 22,043,070 outputs
Outputs from Journal of Cardiovascular Magnetic Resonance (Taylor & Francis Ltd)
#91
of 1,246 outputs
Outputs of similar age
#55,017
of 423,758 outputs
Outputs of similar age from Journal of Cardiovascular Magnetic Resonance (Taylor & Francis Ltd)
#21
of 88 outputs
Altmetric has tracked 22,043,070 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,246 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one has done particularly well, scoring higher than 92% 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 423,758 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 88 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 77% of its contemporaries.