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Standardizing terms for clinical pharmacogenetic test results: consensus terms from the Clinical Pharmacogenetics Implementation Consortium (CPIC)

Overview of attention for article published in Genetics in Medicine, July 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

news
7 news outlets
blogs
1 blog
twitter
48 tweeters
facebook
3 Facebook pages

Citations

dimensions_citation
229 Dimensions

Readers on

mendeley
249 Mendeley
Title
Standardizing terms for clinical pharmacogenetic test results: consensus terms from the Clinical Pharmacogenetics Implementation Consortium (CPIC)
Published in
Genetics in Medicine, July 2016
DOI 10.1038/gim.2016.87
Pubmed ID
Authors

Kelly E. Caudle, Henry M. Dunnenberger, Robert R. Freimuth, Josh F. Peterson, Jonathan D. Burlison, Michelle Whirl-Carrillo, Stuart A. Scott, Heidi L. Rehm, Marc S. Williams, Teri E. Klein, Mary V. Relling, James M. Hoffman

Abstract

Reporting and sharing pharmacogenetic test results across clinical laboratories and electronic health records is a crucial step toward the implementation of clinical pharmacogenetics, but allele function and phenotype terms are not standardized. Our goal was to develop terms that can be broadly applied to characterize pharmacogenetic allele function and inferred phenotypes. Terms currently used by genetic testing laboratories and in the literature were identified. The Clinical Pharmacogenetics Implementation Consortium (CPIC) used the Delphi method to obtain a consensus and agree on uniform terms among pharmacogenetic experts. Experts with diverse involvement in at least one area of pharmacogenetics (clinicians, researchers, genetic testing laboratorians, pharmacogenetics implementers, and clinical informaticians; n = 58) participated. After completion of five surveys, a consensus (>70%) was reached with 90% of experts agreeing to the final sets of pharmacogenetic terms. The proposed standardized pharmacogenetic terms will improve the understanding and interpretation of pharmacogenetic tests and reduce confusion by maintaining consistent nomenclature. These standard terms can also facilitate pharmacogenetic data sharing across diverse electronic health care record systems with clinical decision support.Genet Med advance online publication 21 July 2016Genetics in Medicine (2016); doi:10.1038/gim.2016.87.

Twitter Demographics

The data shown below were collected from the profiles of 48 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 249 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
France 1 <1%
Brazil 1 <1%
Unknown 245 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 61 24%
Student > Master 31 12%
Student > Ph. D. Student 29 12%
Student > Bachelor 24 10%
Other 23 9%
Other 45 18%
Unknown 36 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 49 20%
Pharmacology, Toxicology and Pharmaceutical Science 49 20%
Medicine and Dentistry 45 18%
Agricultural and Biological Sciences 29 12%
Computer Science 6 2%
Other 23 9%
Unknown 48 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 91. 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 17 December 2019.
All research outputs
#253,356
of 16,410,475 outputs
Outputs from Genetics in Medicine
#65
of 2,241 outputs
Outputs of similar age
#7,226
of 267,406 outputs
Outputs of similar age from Genetics in Medicine
#1
of 52 outputs
Altmetric has tracked 16,410,475 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,241 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.3. This one has done particularly well, scoring higher than 97% of its peers.
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 267,406 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.