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Comparison of Machine Learning Models for Prediction of Initial Intravenous Immunoglobulin Resistance in Children With Kawasaki Disease

Overview of attention for article published in Frontiers in Pediatrics, December 2020
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1 X user

Citations

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6 Dimensions

Readers on

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10 Mendeley
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Title
Comparison of Machine Learning Models for Prediction of Initial Intravenous Immunoglobulin Resistance in Children With Kawasaki Disease
Published in
Frontiers in Pediatrics, December 2020
DOI 10.3389/fped.2020.570834
Pubmed ID
Authors

Yasutaka Kuniyoshi, Haruka Tokutake, Natsuki Takahashi, Azusa Kamura, Sumie Yasuda, Makoto Tashiro

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 50%
Other 1 10%
Student > Doctoral Student 1 10%
Student > Master 1 10%
Unknown 2 20%
Readers by discipline Count As %
Computer Science 2 20%
Medicine and Dentistry 2 20%
Neuroscience 1 10%
Business, Management and Accounting 1 10%
Unknown 4 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 04 December 2020.
All research outputs
#20,672,155
of 23,267,128 outputs
Outputs from Frontiers in Pediatrics
#4,303
of 6,257 outputs
Outputs of similar age
#433,381
of 509,311 outputs
Outputs of similar age from Frontiers in Pediatrics
#165
of 275 outputs
Altmetric has tracked 23,267,128 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,257 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 509,311 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 275 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.