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Radiomics-Based Machine Learning in Differentiation Between Glioblastoma and Metastatic Brain Tumors

Overview of attention for article published in Frontiers in oncology, August 2019
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2 X users

Citations

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70 Dimensions

Readers on

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60 Mendeley
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Title
Radiomics-Based Machine Learning in Differentiation Between Glioblastoma and Metastatic Brain Tumors
Published in
Frontiers in oncology, August 2019
DOI 10.3389/fonc.2019.00806
Pubmed ID
Authors

Chaoyue Chen, Xuejin Ou, Jian Wang, Wen Guo, Xuelei Ma

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.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 15%
Other 6 10%
Student > Postgraduate 6 10%
Researcher 5 8%
Student > Ph. D. Student 3 5%
Other 6 10%
Unknown 25 42%
Readers by discipline Count As %
Medicine and Dentistry 15 25%
Computer Science 2 3%
Chemistry 2 3%
Neuroscience 2 3%
Engineering 2 3%
Other 10 17%
Unknown 27 45%
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 23 August 2019.
All research outputs
#20,983,439
of 25,774,185 outputs
Outputs from Frontiers in oncology
#11,533
of 22,796 outputs
Outputs of similar age
#268,681
of 353,059 outputs
Outputs of similar age from Frontiers in oncology
#211
of 347 outputs
Altmetric has tracked 25,774,185 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 22,796 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 28th percentile – i.e., 28% 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 353,059 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 347 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.