| Title |
Transvalvular Ventricular Unloading Before Reperfusion in Acute Myocardial Infarction
|
|---|---|
| Published in |
JACC, August 2020
|
| DOI | 10.1016/j.jacc.2020.06.031 |
| Pubmed ID | |
| Authors |
Lija Swain, Lara Reyelt, Shreyas Bhave, Xiaoying Qiao, Corinne J. Thomas, Elric Zweck, Paige Crowley, Courtney Boggins, Michele Esposito, Michael Chin, Richard H. Karas, William O’Neill, Navin K. Kapur |
X Demographics
The data shown below were collected from the profiles of 131 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 | 63 | 48% |
| Australia | 4 | 3% |
| Spain | 3 | 2% |
| United Kingdom | 2 | 2% |
| Colombia | 2 | 2% |
| India | 2 | 2% |
| Germany | 2 | 2% |
| Italy | 2 | 2% |
| Peru | 2 | 2% |
| Other | 13 | 10% |
| Unknown | 36 | 27% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 87 | 66% |
| Scientists | 19 | 15% |
| Practitioners (doctors, other healthcare professionals) | 18 | 14% |
| Science communicators (journalists, bloggers, editors) | 7 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 47 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 19% |
| Student > Master | 6 | 13% |
| Other | 5 | 11% |
| Student > Ph. D. Student | 5 | 11% |
| Student > Doctoral Student | 3 | 6% |
| Other | 5 | 11% |
| Unknown | 14 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 25 | 53% |
| Biochemistry, Genetics and Molecular Biology | 2 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 2% |
| Computer Science | 1 | 2% |
| Nursing and Health Professions | 1 | 2% |
| Other | 0 | 0% |
| Unknown | 17 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 99. 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 March 2021.
All research outputs
#433,904
of 25,707,225 outputs
Outputs from JACC
#1,047
of 16,909 outputs
Outputs of similar age
#13,283
of 428,065 outputs
Outputs of similar age from JACC
#37
of 249 outputs
Altmetric has tracked 25,707,225 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 16,909 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 30.1. This one has done particularly well, scoring higher than 93% 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 428,065 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 96% of its contemporaries.
We're also able to compare this research output to 249 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.