| Title |
Models predicting the growth response to growth hormone treatment in short children independent of GH status, birth size and gestational age
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, December 2007
|
| DOI | 10.1186/1472-6947-7-40 |
| Pubmed ID | |
| Authors |
Jovanna Dahlgren, Berit Kriström, Aimon Niklasson, Andreas FM Nierop, Sten Rosberg, Kerstin Albertsson-Wikland |
| Abstract |
Mathematical models can be used to predict individual growth responses to growth hormone (GH) therapy. The aim of this study was to construct and validate high-precision models to predict the growth response to GH treatment of short children, independent of their GH status, birth size and gestational age. As the GH doses are included, these models can be used to individualize treatment. |
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 % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| New Zealand | 1 | 3% |
| Unknown | 30 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 19% |
| Researcher | 5 | 16% |
| Student > Bachelor | 4 | 13% |
| Student > Master | 4 | 13% |
| Student > Doctoral Student | 2 | 6% |
| Other | 5 | 16% |
| Unknown | 5 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 18 | 58% |
| Engineering | 2 | 6% |
| Agricultural and Biological Sciences | 2 | 6% |
| Computer Science | 2 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 5 | 16% |
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 15 March 2014.
All research outputs
#18,367,612
of 22,749,166 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,567
of 1,985 outputs
Outputs of similar age
#146,464
of 155,855 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#3
of 3 outputs
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