You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Extraction of echocardiographic data from the electronic medical record is a rapid and efficient method for study of cardiac structure and function
|
|---|---|
| Published in |
Journal of Clinical Bioinformatics, September 2014
|
| DOI | 10.1186/2043-9113-4-12 |
| Pubmed ID | |
| Authors |
Quinn S Wells, Eric Farber-Eger, Dana C Crawford |
| Abstract |
Measures of cardiac structure and function are important human phenotypes that are associated with a range of clinical outcomes. Studying these traits in large populations can be time consuming and costly. Utilizing data from large electronic medical records (EMRs) is one possible solution to this problem. We describe the extraction and filtering of quantitative transthoracic echocardiographic data from the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study, a large, racially diverse, EMR-based cohort (n = 15,863). |
X Demographics
The data shown below were collected from the profiles of 7 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 States | 2 | 29% |
| France | 1 | 14% |
| Unknown | 4 | 57% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 43% |
| Scientists | 2 | 29% |
| Practitioners (doctors, other healthcare professionals) | 2 | 29% |
Mendeley readers
The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 6% |
| Unknown | 33 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 14% |
| Student > Master | 4 | 11% |
| Student > Doctoral Student | 4 | 11% |
| Student > Ph. D. Student | 3 | 9% |
| Other | 2 | 6% |
| Other | 9 | 26% |
| Unknown | 8 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 11 | 31% |
| Agricultural and Biological Sciences | 4 | 11% |
| Computer Science | 4 | 11% |
| Social Sciences | 2 | 6% |
| Psychology | 1 | 3% |
| Other | 3 | 9% |
| Unknown | 10 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 October 2014.
All research outputs
#8,261,756
of 25,373,627 outputs
Outputs from Journal of Clinical Bioinformatics
#19
of 61 outputs
Outputs of similar age
#82,758
of 261,596 outputs
Outputs of similar age from Journal of Clinical Bioinformatics
#1
of 2 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 61 research outputs from this source. They receive a mean Attention Score of 3.1. This one has gotten more attention than average, scoring higher than 68% 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 261,596 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them