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An administrative data validation study of the accuracy of algorithms for identifying rheumatoid arthritis: the influence of the reference standard on algorithm performance

Overview of attention for article published in BMC Musculoskeletal Disorders, June 2014
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  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
2 tweeters

Citations

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55 Dimensions

Readers on

mendeley
88 Mendeley
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Title
An administrative data validation study of the accuracy of algorithms for identifying rheumatoid arthritis: the influence of the reference standard on algorithm performance
Published in
BMC Musculoskeletal Disorders, June 2014
DOI 10.1186/1471-2474-15-216
Pubmed ID
Authors

Jessica Widdifield, Claire Bombardier, Sasha Bernatsky, J Michael Paterson, Diane Green, Jacqueline Young, Noah Ivers, Debra A Butt, R Liisa Jaakkimainen, J Carter Thorne, Karen Tu

Abstract

We have previously validated administrative data algorithms to identify patients with rheumatoid arthritis (RA) using rheumatology clinic records as the reference standard. Here we reassessed the accuracy of the algorithms using primary care records as the reference standard.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 88 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 2 2%
United Kingdom 1 1%
Unknown 85 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 27%
Student > Master 19 22%
Student > Ph. D. Student 10 11%
Other 5 6%
Student > Postgraduate 5 6%
Other 16 18%
Unknown 9 10%
Readers by discipline Count As %
Medicine and Dentistry 40 45%
Social Sciences 9 10%
Pharmacology, Toxicology and Pharmaceutical Science 6 7%
Engineering 4 5%
Psychology 3 3%
Other 15 17%
Unknown 11 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 25 June 2014.
All research outputs
#3,344,526
of 5,036,026 outputs
Outputs from BMC Musculoskeletal Disorders
#1,283
of 1,695 outputs
Outputs of similar age
#77,433
of 124,297 outputs
Outputs of similar age from BMC Musculoskeletal Disorders
#93
of 115 outputs
Altmetric has tracked 5,036,026 research outputs across all sources so far. This one is in the 29th percentile – i.e., 29% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,695 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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 124,297 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 115 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.