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Machine-learning identifies Parkinson's disease patients based on resting-state between-network functional connectivity

Overview of attention for article published in British Journal of Radiology, May 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 (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (86th percentile)

Mentioned by

news
1 news outlet
twitter
5 X users

Citations

dimensions_citation
36 Dimensions

Readers on

mendeley
78 Mendeley
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Title
Machine-learning identifies Parkinson's disease patients based on resting-state between-network functional connectivity
Published in
British Journal of Radiology, May 2019
DOI 10.1259/bjr.20180886
Pubmed ID
Authors

Christian Rubbert, Christian Mathys, Christiane Jockwitz, Christian J Hartmann, Simon B Eickhoff, Felix Hoffstaedter, Svenja Caspers, Claudia R Eickhoff, Benjamin Sigl, Nikolas A Teichert, Martin Südmeyer, Bernd Turowski, Alfons Schnitzler, Julian Caspers

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 78 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 13%
Student > Doctoral Student 10 13%
Student > Bachelor 7 9%
Researcher 7 9%
Student > Master 7 9%
Other 15 19%
Unknown 22 28%
Readers by discipline Count As %
Neuroscience 12 15%
Medicine and Dentistry 11 14%
Computer Science 6 8%
Psychology 5 6%
Engineering 3 4%
Other 9 12%
Unknown 32 41%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 07 September 2019.
All research outputs
#2,985,850
of 25,394,764 outputs
Outputs from British Journal of Radiology
#159
of 3,298 outputs
Outputs of similar age
#61,352
of 365,341 outputs
Outputs of similar age from British Journal of Radiology
#6
of 43 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,298 research outputs from this source. They receive a mean Attention Score of 4.4. This one has done particularly well, scoring higher than 95% 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 365,341 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 83% of its contemporaries.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.