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A Bayesian Model to Predict COVID-19 Severity in Children

Overview of attention for article published in The Pediatric Infectious Disease Journal, July 2021
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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 (93rd percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

Mentioned by

blogs
1 blog
twitter
44 X users
facebook
1 Facebook page

Citations

dimensions_citation
20 Dimensions

Readers on

mendeley
59 Mendeley
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Title
A Bayesian Model to Predict COVID-19 Severity in Children
Published in
The Pediatric Infectious Disease Journal, July 2021
DOI 10.1097/inf.0000000000003204
Pubmed ID
Authors

Sara Domínguez-Rodríguez, Serena Villaverde, Francisco J. Sanz-Santaeufemia, Carlos Grasa, Antoni Soriano-Arandes, Jesús Saavedra-Lozano, Victoria Fumadó, Cristina Epalza, Miquel Serna-Pascual, José A. Alonso-Cadenas, Paula Rodríguez-Molino, Joan Pujol-Morro, David Aguilera-Alonso, Silvia Simó, Sara Villanueva-Medina, M. Isabel Iglesias-Bouzas, M. José Mellado, Blanca Herrero, Susana Melendo, Mercedes De la Torre, Teresa Del Rosal, Pere Soler-Palacin, Cristina Calvo, María Urretavizcaya-Martínez, Marta Pareja, Fátima Ara-Montojo, Yolanda Ruiz del Prado, Nerea Gallego, Marta Illán Ramos, Elena Cobos, Alfredo Tagarro, Cinta Moraleda, on behalf of EPICO-AEP Working Group

X Demographics

X Demographics

The data shown below were collected from the profiles of 44 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 59 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 59 100%

Demographic breakdown

Readers by professional status Count As %
Other 8 14%
Student > Bachelor 6 10%
Student > Ph. D. Student 5 8%
Researcher 4 7%
Student > Master 4 7%
Other 14 24%
Unknown 18 31%
Readers by discipline Count As %
Medicine and Dentistry 18 31%
Nursing and Health Professions 5 8%
Computer Science 3 5%
Pharmacology, Toxicology and Pharmaceutical Science 2 3%
Biochemistry, Genetics and Molecular Biology 1 2%
Other 4 7%
Unknown 26 44%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 35. 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 01 June 2022.
All research outputs
#1,150,144
of 25,571,620 outputs
Outputs from The Pediatric Infectious Disease Journal
#200
of 6,302 outputs
Outputs of similar age
#28,340
of 447,987 outputs
Outputs of similar age from The Pediatric Infectious Disease Journal
#7
of 88 outputs
Altmetric has tracked 25,571,620 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,302 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.9. 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 447,987 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 93% of its contemporaries.
We're also able to compare this research output to 88 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 93% of its contemporaries.