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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Machine Learning Improves Cardiovascular Risk Definition for Young, Asymptomatic Individuals
|
|---|---|
| Published in |
JACC, October 2020
|
| DOI | 10.1016/j.jacc.2020.08.017 |
| Pubmed ID | |
| Authors |
Fátima Sánchez-Cabo, Xavier Rossello, Valentín Fuster, Fernando Benito, Jose Pedro Manzano, Juan Carlos Silla, Juan Miguel Fernández-Alvira, Belén Oliva, Leticia Fernández-Friera, Beatriz López-Melgar, José María Mendiguren, Javier Sanz, Jose María Ordovás, Vicente Andrés, Antonio Fernández-Ortiz, Héctor Bueno, Borja Ibáñez, José Manuel García-Ruiz, Enrique Lara-Pezzi |
X Demographics
The data shown below were collected from the profiles of 64 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 27 | 42% |
| United States | 8 | 13% |
| France | 5 | 8% |
| Italy | 2 | 3% |
| Mexico | 1 | 2% |
| Romania | 1 | 2% |
| Peru | 1 | 2% |
| Unknown | 19 | 30% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 43 | 67% |
| Scientists | 12 | 19% |
| Practitioners (doctors, other healthcare professionals) | 7 | 11% |
| Science communicators (journalists, bloggers, editors) | 2 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 111 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 17 | 15% |
| Student > Master | 11 | 10% |
| Student > Doctoral Student | 11 | 10% |
| Student > Ph. D. Student | 7 | 6% |
| Student > Bachelor | 6 | 5% |
| Other | 16 | 14% |
| Unknown | 43 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 27 | 24% |
| Computer Science | 10 | 9% |
| Nursing and Health Professions | 6 | 5% |
| Biochemistry, Genetics and Molecular Biology | 5 | 5% |
| Engineering | 4 | 4% |
| Other | 10 | 9% |
| Unknown | 49 | 44% |
Attention Score in Context
This research output has an Altmetric Attention Score of 145. 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 05 August 2023.
All research outputs
#290,114
of 25,732,188 outputs
Outputs from JACC
#657
of 16,923 outputs
Outputs of similar age
#8,701
of 434,111 outputs
Outputs of similar age from JACC
#21
of 197 outputs
Altmetric has tracked 25,732,188 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 16,923 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 96% 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 434,111 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 197 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.