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Development of an Early Warning Model for Predicting the Death Risk of Coronavirus Disease 2019 Based on Data Immediately Available on Admission

Overview of attention for article published in Frontiers in Medicine, August 2021
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13 Mendeley
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Title
Development of an Early Warning Model for Predicting the Death Risk of Coronavirus Disease 2019 Based on Data Immediately Available on Admission
Published in
Frontiers in Medicine, August 2021
DOI 10.3389/fmed.2021.699243
Pubmed ID
Authors

Hai Wang, Haibo Ai, Yunong Fu, Qinglin Li, Ruixia Cui, Xiaohua Ma, Yan-fen Ma, Zi Wang, Tong Liu, Yunxiang Long, Kai Qu, Chang Liu, Jingyao Zhang

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 15%
Student > Ph. D. Student 1 8%
Student > Postgraduate 1 8%
Student > Bachelor 1 8%
Unknown 8 62%
Readers by discipline Count As %
Medicine and Dentistry 3 23%
Economics, Econometrics and Finance 1 8%
Environmental Science 1 8%
Unknown 8 62%
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 08 September 2021.
All research outputs
#18,145,205
of 23,310,485 outputs
Outputs from Frontiers in Medicine
#3,823
of 5,971 outputs
Outputs of similar age
#294,418
of 432,634 outputs
Outputs of similar age from Frontiers in Medicine
#282
of 427 outputs
Altmetric has tracked 23,310,485 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,971 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.5. This one is in the 30th percentile – i.e., 30% 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 432,634 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 427 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.