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Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record

Overview of attention for article published in Arthritis Research & Therapy, December 2019
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (64th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
6 tweeters

Citations

dimensions_citation
1 Dimensions

Readers on

mendeley
16 Mendeley
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Title
Rule-based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record
Published in
Arthritis Research & Therapy, December 2019
DOI 10.1186/s13075-019-2092-7
Pubmed ID
Authors

Lia Jamian, Lee Wheless, Leslie J. Crofford, April Barnado

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 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 31%
Student > Ph. D. Student 3 19%
Researcher 2 13%
Lecturer > Senior Lecturer 1 6%
Student > Bachelor 1 6%
Other 1 6%
Unknown 3 19%
Readers by discipline Count As %
Medicine and Dentistry 9 56%
Business, Management and Accounting 1 6%
Computer Science 1 6%
Nursing and Health Professions 1 6%
Unknown 4 25%

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 07 January 2020.
All research outputs
#4,351,285
of 15,383,358 outputs
Outputs from Arthritis Research & Therapy
#1,034
of 2,411 outputs
Outputs of similar age
#118,650
of 341,856 outputs
Outputs of similar age from Arthritis Research & Therapy
#151
of 228 outputs
Altmetric has tracked 15,383,358 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 2,411 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one has gotten more attention than average, scoring higher than 56% 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 341,856 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 64% of its contemporaries.
We're also able to compare this research output to 228 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.