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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 (91st percentile)

Mentioned by

blogs
1 blog
twitter
45 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
51 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

Twitter Demographics

The data shown below were collected from the profiles of 45 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 51 100%

Demographic breakdown

Readers by professional status Count As %
Other 8 16%
Student > Ph. D. Student 5 10%
Librarian 4 8%
Student > Bachelor 4 8%
Researcher 3 6%
Other 14 27%
Unknown 13 25%
Readers by discipline Count As %
Medicine and Dentistry 17 33%
Nursing and Health Professions 4 8%
Computer Science 3 6%
Social Sciences 2 4%
Pharmacology, Toxicology and Pharmaceutical Science 2 4%
Other 3 6%
Unknown 20 39%

Attention Score in Context

This research output has an Altmetric Attention Score of 36. 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
#940,572
of 22,570,871 outputs
Outputs from The Pediatric Infectious Disease Journal
#163
of 5,860 outputs
Outputs of similar age
#22,193
of 341,847 outputs
Outputs of similar age from The Pediatric Infectious Disease Journal
#7
of 73 outputs
Altmetric has tracked 22,570,871 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 5,860 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.0. This one has done particularly well, scoring higher than 97% 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 341,847 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 73 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 91% of its contemporaries.