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A computational method to quantitatively measure pediatric drug safety using electronic medical records

Overview of attention for article published in BMC Medical Research Methodology, January 2020
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Mentioned by

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1 X user

Citations

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1 Dimensions

Readers on

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23 Mendeley
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Title
A computational method to quantitatively measure pediatric drug safety using electronic medical records
Published in
BMC Medical Research Methodology, January 2020
DOI 10.1186/s12874-020-0902-x
Pubmed ID
Authors

Gang Yu, Xian Zeng, Shaoqing Ni, Zheng Jia, Weihong Chen, Xudong Lu, Jiye An, Huilong Duan, Qiang Shu, Haomin Li

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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 17%
Researcher 2 9%
Student > Bachelor 1 4%
Student > Doctoral Student 1 4%
Student > Postgraduate 1 4%
Other 0 0%
Unknown 14 61%
Readers by discipline Count As %
Medicine and Dentistry 2 9%
Unspecified 1 4%
Nursing and Health Professions 1 4%
Psychology 1 4%
Computer Science 1 4%
Other 2 9%
Unknown 15 65%
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 16 January 2020.
All research outputs
#20,599,981
of 23,186,937 outputs
Outputs from BMC Medical Research Methodology
#1,907
of 2,045 outputs
Outputs of similar age
#381,990
of 456,833 outputs
Outputs of similar age from BMC Medical Research Methodology
#48
of 51 outputs
Altmetric has tracked 23,186,937 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 2,045 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. 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 456,833 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 51 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.