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Understanding and preventing type 1 diabetes through the unique working model of TrialNet

Overview of attention for article published in Diabetologia, August 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (59th percentile)

Mentioned by

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33 X users
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2 Facebook pages

Citations

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57 Dimensions

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62 Mendeley
Title
Understanding and preventing type 1 diabetes through the unique working model of TrialNet
Published in
Diabetologia, August 2017
DOI 10.1007/s00125-017-4384-2
Pubmed ID
Authors

Manuela Battaglia, Mark S. Anderson, Jane H. Buckner, Susan M. Geyer, Peter A. Gottlieb, Thomas W. H. Kay, Åke Lernmark, Sarah Muller, Alberto Pugliese, Bart O. Roep, Carla J. Greenbaum, Mark Peakman

Abstract

Type 1 diabetes is an autoimmune disease arising from the destruction of pancreatic insulin-producing beta cells. The disease represents a continuum, progressing sequentially at variable rates through identifiable stages prior to the onset of symptoms, through diagnosis and into the critical periods that follow, culminating in a variable depth of beta cell depletion. The ability to identify the very earliest of these presymptomatic stages has provided a setting in which prevention strategies can be trialled, as well as furnishing an unprecedented opportunity to study disease evolution, including intrinsic and extrinsic initiators and drivers. This niche opportunity is occupied by Type 1 Diabetes TrialNet, an international consortium of clinical trial centres that leads the field in intervention and prevention studies, accompanied by deep longitudinal bio-sampling. In this review, we focus on discoveries arising from this unique bioresource, comprising more than 70,000 samples, and outline the processes and science that have led to new biomarkers and mechanistic insights, as well as identifying new challenges and opportunities. We conclude that via integration of clinical trials and mechanistic studies, drawing in clinicians and scientists and developing partnership with industry, TrialNet embodies an enviable and unique working model for understanding a disease that to date has no cure and for designing new therapeutic approaches.

X Demographics

X Demographics

The data shown below were collected from the profiles of 33 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 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 62 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 19%
Researcher 9 15%
Student > Bachelor 9 15%
Other 4 6%
Student > Doctoral Student 3 5%
Other 9 15%
Unknown 16 26%
Readers by discipline Count As %
Medicine and Dentistry 22 35%
Biochemistry, Genetics and Molecular Biology 10 16%
Agricultural and Biological Sciences 3 5%
Nursing and Health Professions 2 3%
Immunology and Microbiology 2 3%
Other 5 8%
Unknown 18 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 24. 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 30 June 2021.
All research outputs
#1,390,177
of 23,005,189 outputs
Outputs from Diabetologia
#772
of 5,089 outputs
Outputs of similar age
#29,902
of 317,599 outputs
Outputs of similar age from Diabetologia
#38
of 94 outputs
Altmetric has tracked 23,005,189 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,089 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.7. This one has done well, scoring higher than 84% 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 317,599 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 94 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 59% of its contemporaries.