| Title |
Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting
|
|---|---|
| Published in |
Genome Medicine, May 2012
|
| DOI | 10.1186/gm347 |
| Pubmed ID | |
| Authors |
David K Crockett, Perry G Ridge, Andrew R Wilson, Elaine Lyon, Marc S Williams, Scott P Narus, Julio C Facelli, Joyce A Mitchell |
| Abstract |
Accurate interpretation of gene testing is a key component in customizing patient therapy. Where confirming evidence for a gene variant is lacking, computational prediction may be employed. A standardized framework, however, does not yet exist for quantitative evaluation of disease association for uncertain or novel gene variants in an objective manner. Here, complementary predictors for missense gene variants were incorporated into a weighted Consensus framework that includes calculated reference intervals from known disease outcomes. Data visualization for clinical reporting is also discussed. |
X Demographics
The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 33% |
| United States | 2 | 22% |
| France | 1 | 11% |
| Germany | 1 | 11% |
| Unknown | 2 | 22% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 4 | 44% |
| Members of the public | 4 | 44% |
| Practitioners (doctors, other healthcare professionals) | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 9% |
| Belgium | 2 | 4% |
| Canada | 1 | 2% |
| Unknown | 45 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 17 | 32% |
| Other | 7 | 13% |
| Student > Ph. D. Student | 7 | 13% |
| Student > Master | 5 | 9% |
| Professor > Associate Professor | 3 | 6% |
| Other | 8 | 15% |
| Unknown | 6 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 18 | 34% |
| Biochemistry, Genetics and Molecular Biology | 9 | 17% |
| Medicine and Dentistry | 7 | 13% |
| Computer Science | 6 | 11% |
| Psychology | 2 | 4% |
| Other | 3 | 6% |
| Unknown | 8 | 15% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 11 January 2016.
All research outputs
#2,418,536
of 22,665,794 outputs
Outputs from Genome Medicine
#562
of 1,432 outputs
Outputs of similar age
#16,470
of 165,043 outputs
Outputs of similar age from Genome Medicine
#3
of 18 outputs
Altmetric has tracked 22,665,794 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,432 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.5. This one has gotten more attention than average, scoring higher than 60% of its peers.
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We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.