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Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning

Overview of attention for article published in BMC Musculoskeletal Disorders, December 2013
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
33 Mendeley
Title
Analysis of mass spectrometry data from the secretome of an explant model of articular cartilage exposed to pro-inflammatory and anti-inflammatory stimuli using machine learning
Published in
BMC Musculoskeletal Disorders, December 2013
DOI 10.1186/1471-2474-14-349
Pubmed ID
Authors

Anna L Swan, Kirsty L Hillier, Julia R Smith, David Allaway, Susan Liddell, Jaume Bacardit, Ali Mobasheri

Abstract

Osteoarthritis (OA) is an inflammatory disease of synovial joints involving the loss and degeneration of articular cartilage. The gold standard for evaluating cartilage loss in OA is the measurement of joint space width on standard radiographs. However, in most cases the diagnosis is made well after the onset of the disease, when the symptoms are well established. Identification of early biomarkers of OA can facilitate earlier diagnosis, improve disease monitoring and predict responses to therapeutic interventions.

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

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Unknown 32 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 21%
Researcher 6 18%
Unspecified 5 15%
Professor > Associate Professor 3 9%
Student > Postgraduate 2 6%
Other 10 30%
Readers by discipline Count As %
Unspecified 8 24%
Medicine and Dentistry 6 18%
Agricultural and Biological Sciences 5 15%
Biochemistry, Genetics and Molecular Biology 4 12%
Engineering 4 12%
Other 6 18%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 April 2018.
All research outputs
#7,212,094
of 12,788,180 outputs
Outputs from BMC Musculoskeletal Disorders
#1,271
of 2,547 outputs
Outputs of similar age
#105,786
of 245,935 outputs
Outputs of similar age from BMC Musculoskeletal Disorders
#175
of 346 outputs
Altmetric has tracked 12,788,180 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 2,547 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.9. This one is in the 47th percentile – i.e., 47% 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 245,935 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 55% of its contemporaries.
We're also able to compare this research output to 346 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.