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An algorithm to predict phenotypic severity in mucopolysaccharidosis type I in the first month of life

Overview of attention for article published in Orphanet Journal of Rare Diseases, July 2013
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Title
An algorithm to predict phenotypic severity in mucopolysaccharidosis type I in the first month of life
Published in
Orphanet Journal of Rare Diseases, July 2013
DOI 10.1186/1750-1172-8-99
Pubmed ID
Authors

Sandra DK Kingma, Eveline J Langereis, Clasine M de Klerk, Lida Zoetekouw, Tom Wagemans, Lodewijk IJlst, Ronald JA Wanders, Frits A Wijburg, Naomi van Vlies

Abstract

Mucopolysaccharidosis type I (MPS I) is a progressive multisystem lysosomal storage disease caused by deficiency of the enzyme alpha-L-iduronidase (IDUA). Patients present with a continuous spectrum of disease severity, and the most severely affected patients (Hurler phenotype; MPS I-H) develop progressive cognitive impairment. The treatment of choice for MPS I-H patients is haematopoietic stem cell transplantation, while patients with the more attenuated phenotypes benefit from enzyme replacement therapy.The potential of newborn screening (NBS) for MPS I is currently studied in many countries. NBS for MPS I, however, necessitates early assessment of the phenotype, in order to decide on the appropriate treatment. In this study, we developed an algorithm to predict phenotypic severity in newborn MPS I patients.

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The data shown below were collected from the profiles of 2 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 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 2%
Unknown 63 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 19%
Researcher 12 19%
Student > Master 9 14%
Student > Doctoral Student 5 8%
Student > Bachelor 4 6%
Other 8 13%
Unknown 14 22%
Readers by discipline Count As %
Medicine and Dentistry 23 36%
Biochemistry, Genetics and Molecular Biology 11 17%
Agricultural and Biological Sciences 4 6%
Psychology 2 3%
Nursing and Health Professions 1 2%
Other 6 9%
Unknown 17 27%
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 13 July 2013.
All research outputs
#14,755,656
of 22,714,025 outputs
Outputs from Orphanet Journal of Rare Diseases
#1,686
of 2,603 outputs
Outputs of similar age
#115,618
of 194,246 outputs
Outputs of similar age from Orphanet Journal of Rare Diseases
#20
of 33 outputs
Altmetric has tracked 22,714,025 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 2,603 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.5. This one is in the 32nd percentile – i.e., 32% 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 194,246 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.