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Can Synovial Pathobiology Integrate with Current Clinical and Imaging Prediction Models to Achieve Personalized Health Care in Rheumatoid Arthritis?

Overview of attention for article published in Frontiers in Medicine, May 2017
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  • Above-average Attention Score compared to outputs of the same age and source (57th percentile)

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4 X users

Citations

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14 Dimensions

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25 Mendeley
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Title
Can Synovial Pathobiology Integrate with Current Clinical and Imaging Prediction Models to Achieve Personalized Health Care in Rheumatoid Arthritis?
Published in
Frontiers in Medicine, May 2017
DOI 10.3389/fmed.2017.00041
Pubmed ID
Authors

Frances Claire Humby, Farida Al Balushi, Gloria Lliso, Alberto Cauli, Costantino Pitzalis

Abstract

Although great progress has been made in the past decade toward understanding the pathogenesis of rheumatoid arthritis (RA), clinicians remain some distance from a goal of personalized health care. The capacity to diagnose RA early, predict prognosis, and moreover predict response to biologic therapies has been a research focus for many years. How currently available clinical prediction models can facilitate such goals is reviewed in this article. In addition, the role of current imaging techniques in this regard is also discussed. Finally, the authors review the current literature regarding synovial biomarkers and consider whether integration of synovial pathobiology into clinical prediction algorithms may enhance their predictive value.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 16%
Student > Doctoral Student 4 16%
Researcher 4 16%
Student > Postgraduate 2 8%
Professor > Associate Professor 2 8%
Other 4 16%
Unknown 5 20%
Readers by discipline Count As %
Medicine and Dentistry 11 44%
Immunology and Microbiology 4 16%
Biochemistry, Genetics and Molecular Biology 1 4%
Computer Science 1 4%
Unknown 8 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 12 October 2018.
All research outputs
#13,200,078
of 22,968,808 outputs
Outputs from Frontiers in Medicine
#1,978
of 5,735 outputs
Outputs of similar age
#152,095
of 310,917 outputs
Outputs of similar age from Frontiers in Medicine
#20
of 49 outputs
Altmetric has tracked 22,968,808 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,735 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.0. 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 310,917 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 50% of its contemporaries.
We're also able to compare this research output to 49 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 57% of its contemporaries.