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Prediction models for short children born small for gestational age (SGA) covering the total growth phase. Analyses based on data from KIGS (Pfizer International Growth Database)

Overview of attention for article published in BMC Medical Informatics and Decision Making, June 2011
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Title
Prediction models for short children born small for gestational age (SGA) covering the total growth phase. Analyses based on data from KIGS (Pfizer International Growth Database)
Published in
BMC Medical Informatics and Decision Making, June 2011
DOI 10.1186/1472-6947-11-38
Pubmed ID
Authors

Michael B Ranke, Anders Lindberg, KIGS International Board

Abstract

Mathematical models can be developed to predict growth in short children treated with growth hormone (GH). These models can serve to optimize and individualize treatment in terms of height outcomes and costs. The aims of this study were to compile existing prediction models for short children born SGA (SGA), to develop new models and to validate the algorithms.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 3%
Unknown 37 97%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 10 26%
Researcher 8 21%
Student > Doctoral Student 3 8%
Other 2 5%
Student > Ph. D. Student 2 5%
Other 6 16%
Unknown 7 18%
Readers by discipline Count As %
Medicine and Dentistry 20 53%
Nursing and Health Professions 3 8%
Agricultural and Biological Sciences 3 8%
Economics, Econometrics and Finance 2 5%
Environmental Science 1 3%
Other 0 0%
Unknown 9 24%
Attention Score in Context

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 04 August 2013.
All research outputs
#14,755,656
of 22,715,151 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,227
of 1,982 outputs
Outputs of similar age
#81,246
of 111,251 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#13
of 20 outputs
Altmetric has tracked 22,715,151 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,982 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 34th percentile – i.e., 34% 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 111,251 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.