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Can We Identify Patients with High Risk of Osteoarthritis Progression Who Will Respond to Treatment? A Focus on Biomarkers and Frailty

Overview of attention for article published in Drugs & Aging, June 2015
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Mentioned by

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3 tweeters

Citations

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15 Dimensions

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90 Mendeley
Title
Can We Identify Patients with High Risk of Osteoarthritis Progression Who Will Respond to Treatment? A Focus on Biomarkers and Frailty
Published in
Drugs & Aging, June 2015
DOI 10.1007/s40266-015-0276-7
Pubmed ID
Authors

Nigel Arden, Pascal Richette, Cyrus Cooper, Olivier Bruyère, Eric Abadie, Jaime Branco, Maria Luisa Brandi, Francis Berenbaum, Cécile Clerc, Elaine Dennison, Jean-Pierre Devogelaer, Marc Hochberg, Pieter D’Hooghe, Gabriel Herrero-Beaumont, John A. Kanis, Andrea Laslop, Véronique Leblanc, Stefania Maggi, Giuseppe Mautone, Jean-Pierre Pelletier, Florence Petit-Dop, Susanne Reiter-Niesert, René Rizzoli, Lucio Rovati, Eleonora Tajana Messi, Yannis Tsouderos, Johanne Martel-Pelletier, Jean-Yves Reginster

Abstract

Osteoarthritis (OA), a disease affecting different patient phenotypes, appears as an optimal candidate for personalized healthcare. The aim of the discussions of the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) working group was to explore the value of markers of different sources in defining different phenotypes of patients with OA. The ESCEO organized a series of meetings to explore the possibility of identifying patients who would most benefit from treatment for OA, on the basis of recent data and expert opinion. In the first meeting, patient phenotypes were identified according to the number of affected joints, biomechanical factors, and the presence of lesions in the subchondral bone. In the second meeting, summarized in the present article, the working group explored other markers involved in OA. Profiles of patients may be defined according to their level of pain, functional limitation, and presence of coexistent chronic conditions including frailty status. A considerable amount of data suggests that magnetic resonance imaging may also assist in delineating different phenotypes of patients with OA. Among multiple biochemical biomarkers identified, none is sufficiently validated and recognized to identify patients who should be treated. Considerable efforts are also being made to identify genetic and epigenetic factors involved in OA, but results are still limited. The many potential biomarkers that could be used as potential stratifiers are promising, but more research is needed to characterize and qualify the existing biomarkers and to identify new candidates.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
Portugal 1 1%
Italy 1 1%
United Kingdom 1 1%
Finland 1 1%
Unknown 85 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 20%
Researcher 16 18%
Student > Bachelor 13 14%
Unspecified 9 10%
Student > Master 8 9%
Other 26 29%
Readers by discipline Count As %
Medicine and Dentistry 40 44%
Unspecified 14 16%
Nursing and Health Professions 10 11%
Agricultural and Biological Sciences 5 6%
Engineering 5 6%
Other 16 18%

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 10 September 2015.
All research outputs
#7,871,154
of 12,545,170 outputs
Outputs from Drugs & Aging
#660
of 841 outputs
Outputs of similar age
#123,076
of 232,533 outputs
Outputs of similar age from Drugs & Aging
#9
of 12 outputs
Altmetric has tracked 12,545,170 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 841 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 16th percentile – i.e., 16% 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 232,533 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.