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Developing and validating risk prediction models in an individual participant data meta-analysis

Overview of attention for article published in BMC Medical Research Methodology, January 2014
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (56th percentile)

Mentioned by

twitter
2 tweeters

Citations

dimensions_citation
41 Dimensions

Readers on

mendeley
91 Mendeley
Title
Developing and validating risk prediction models in an individual participant data meta-analysis
Published in
BMC Medical Research Methodology, January 2014
DOI 10.1186/1471-2288-14-3
Pubmed ID
Authors

Ikhlaaq Ahmed, Thomas PA Debray, Karel GM Moons, Richard D Riley

Abstract

Risk prediction models estimate the risk of developing future outcomes for individuals based on one or more underlying characteristics (predictors). We review how researchers develop and validate risk prediction models within an individual participant data (IPD) meta-analysis, in order to assess the feasibility and conduct of the approach.

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

Geographical breakdown

Country Count As %
United Kingdom 2 2%
New Zealand 1 1%
United States 1 1%
Unknown 87 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 28 31%
Researcher 18 20%
Student > Postgraduate 7 8%
Student > Master 6 7%
Student > Doctoral Student 5 5%
Other 16 18%
Unknown 11 12%
Readers by discipline Count As %
Medicine and Dentistry 36 40%
Computer Science 7 8%
Agricultural and Biological Sciences 5 5%
Engineering 4 4%
Mathematics 4 4%
Other 16 18%
Unknown 19 21%

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 16 February 2014.
All research outputs
#2,320,547
of 5,039,474 outputs
Outputs from BMC Medical Research Methodology
#338
of 606 outputs
Outputs of similar age
#54,638
of 134,726 outputs
Outputs of similar age from BMC Medical Research Methodology
#21
of 31 outputs
Altmetric has tracked 5,039,474 research outputs across all sources so far. This one has received more attention than most of these and is in the 51st percentile.
So far Altmetric has tracked 606 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 40th percentile – i.e., 40% 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 134,726 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 56% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.