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Hip Fracture Discrimination Based on Statistical Multi-parametric Modeling (SMPM)

Overview of attention for article published in Annals of Biomedical Engineering, May 2019
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

twitter
6 tweeters

Readers on

mendeley
5 Mendeley
Title
Hip Fracture Discrimination Based on Statistical Multi-parametric Modeling (SMPM)
Published in
Annals of Biomedical Engineering, May 2019
DOI 10.1007/s10439-019-02298-x
Pubmed ID
Authors

Julio Carballido-Gamio, Aihong Yu, Ling Wang, Yongbin Su, Andrew J. Burghardt, Thomas F. Lang, Xiaoguang Cheng

Twitter Demographics

The data shown below were collected from the profiles of 6 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 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Other 1 20%
Student > Doctoral Student 1 20%
Professor 1 20%
Student > Master 1 20%
Student > Postgraduate 1 20%
Other 0 0%
Readers by discipline Count As %
Medicine and Dentistry 3 60%
Engineering 2 40%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 November 2019.
All research outputs
#4,159,884
of 13,941,162 outputs
Outputs from Annals of Biomedical Engineering
#374
of 1,413 outputs
Outputs of similar age
#97,248
of 254,938 outputs
Outputs of similar age from Annals of Biomedical Engineering
#10
of 30 outputs
Altmetric has tracked 13,941,162 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,413 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has gotten more attention than average, scoring higher than 73% 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 254,938 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 61% of its contemporaries.
We're also able to compare this research output to 30 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 66% of its contemporaries.