↓ Skip to main content

Genetic diversity and pathogenic variants as possible predictors of severity in a French sample of nonsyndromic heritable thoracic aortic aneurysms and dissections (nshTAAD)

Overview of attention for article published in Genetics in Medicine, February 2019
Altmetric Badge

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 (84th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
20 X users
facebook
3 Facebook pages

Citations

dimensions_citation
41 Dimensions

Readers on

mendeley
29 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Genetic diversity and pathogenic variants as possible predictors of severity in a French sample of nonsyndromic heritable thoracic aortic aneurysms and dissections (nshTAAD)
Published in
Genetics in Medicine, February 2019
DOI 10.1038/s41436-019-0444-y
Pubmed ID
Authors

Pauline Arnaud, Nadine Hanna, Louise Benarroch, Mélodie Aubart, Laurence Bal, Patrice Bouvagnet, Tiffany Busa, Yves Dulac, Sophie Dupuis-Girod, Thomas Edouard, Laurence Faivre, Laurent Gouya, Didier Lacombe, Maud Langeois, Bruno Leheup, Olivier Milleron, Sophie Naudion, Sylvie Odent, Maria Tchitchinadze, Jacques Ropers, Guillaume Jondeau, Catherine Boileau

X Demographics

X Demographics

The data shown below were collected from the profiles of 20 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 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 21%
Unspecified 4 14%
Student > Ph. D. Student 3 10%
Other 2 7%
Student > Postgraduate 2 7%
Other 4 14%
Unknown 8 28%
Readers by discipline Count As %
Medicine and Dentistry 10 34%
Unspecified 4 14%
Biochemistry, Genetics and Molecular Biology 3 10%
Computer Science 2 7%
Nursing and Health Professions 1 3%
Other 1 3%
Unknown 8 28%
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 11 February 2020.
All research outputs
#3,050,639
of 25,385,509 outputs
Outputs from Genetics in Medicine
#1,042
of 2,945 outputs
Outputs of similar age
#71,393
of 455,881 outputs
Outputs of similar age from Genetics in Medicine
#30
of 60 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,945 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.0. This one has gotten more attention than average, scoring higher than 64% 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 455,881 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 84% of its contemporaries.
We're also able to compare this research output to 60 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.