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Development and Validation of a Deep Learning Radiomics Model Predicting Lymph Node Status in Operable Cervical Cancer

Overview of attention for article published in Frontiers in oncology, April 2020
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Mentioned by

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1 X user

Citations

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29 Dimensions

Readers on

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49 Mendeley
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Title
Development and Validation of a Deep Learning Radiomics Model Predicting Lymph Node Status in Operable Cervical Cancer
Published in
Frontiers in oncology, April 2020
DOI 10.3389/fonc.2020.00464
Pubmed ID
Authors

Taotao Dong, Chun Yang, Baoxia Cui, Ting Zhang, Xiubin Sun, Kun Song, Linlin Wang, Beihua Kong, Xingsheng Yang

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

Geographical breakdown

Country Count As %
Unknown 49 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 20%
Student > Postgraduate 6 12%
Researcher 3 6%
Lecturer 1 2%
Student > Doctoral Student 1 2%
Other 4 8%
Unknown 24 49%
Readers by discipline Count As %
Medicine and Dentistry 9 18%
Engineering 4 8%
Agricultural and Biological Sciences 2 4%
Computer Science 2 4%
Psychology 2 4%
Other 4 8%
Unknown 26 53%
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 19 May 2020.
All research outputs
#22,771,990
of 25,387,668 outputs
Outputs from Frontiers in oncology
#15,926
of 22,433 outputs
Outputs of similar age
#348,595
of 403,227 outputs
Outputs of similar age from Frontiers in oncology
#312
of 471 outputs
Altmetric has tracked 25,387,668 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 22,433 research outputs from this source. They receive a mean Attention Score of 3.0. 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 403,227 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 471 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.