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Mendeley readers
Attention Score in Context
Title |
The ARIC predictive model reliably predicted risk of type II diabetes in Asian populations
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Published in |
BMC Medical Research Methodology, April 2012
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DOI | 10.1186/1471-2288-12-48 |
Pubmed ID | |
Authors |
Calvin Woon-Loong Chin, Elian Hui San Chia, Stefan Ma, Derrick Heng, Maudrene Tan, Jeanette Lee, E Shyong Tai, Agus Salim |
Abstract |
Identification of high-risk individuals is crucial for effective implementation of type 2 diabetes mellitus prevention programs. Several studies have shown that multivariable predictive functions perform as well as the 2-hour post-challenge glucose in identifying these high-risk individuals. The performance of these functions in Asian populations, where the rise in prevalence of type 2 diabetes mellitus is expected to be the greatest in the next several decades, is relatively unknown. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Singapore | 1 | 2% |
Unknown | 41 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 9 | 21% |
Student > Bachelor | 7 | 17% |
Student > Master | 5 | 12% |
Student > Postgraduate | 4 | 10% |
Student > Ph. D. Student | 2 | 5% |
Other | 4 | 10% |
Unknown | 11 | 26% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 18 | 43% |
Nursing and Health Professions | 4 | 10% |
Agricultural and Biological Sciences | 3 | 7% |
Social Sciences | 1 | 2% |
Biochemistry, Genetics and Molecular Biology | 1 | 2% |
Other | 2 | 5% |
Unknown | 13 | 31% |
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 14 April 2012.
All research outputs
#18,305,445
of 22,664,267 outputs
Outputs from BMC Medical Research Methodology
#1,726
of 2,000 outputs
Outputs of similar age
#124,280
of 161,293 outputs
Outputs of similar age from BMC Medical Research Methodology
#28
of 34 outputs
Altmetric has tracked 22,664,267 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,000 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.2. This one is in the 6th percentile – i.e., 6% 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 161,293 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 8th percentile – i.e., 8% of its contemporaries scored the same or lower than it.