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Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients

Overview of attention for article published in Radiation Oncology, December 2022
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2 X users

Citations

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2 Dimensions

Readers on

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9 Mendeley
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Title
Machine learning-based normal tissue complication probability model for predicting albumin-bilirubin (ALBI) grade increase in hepatocellular carcinoma patients
Published in
Radiation Oncology, December 2022
DOI 10.1186/s13014-022-02138-8
Pubmed ID
Authors

Anussara Prayongrat, Natchalee Srimaneekarn, Kanokporn Thonglert, Chonlakiet Khorprasert, Napapat Amornwichet, Petch Alisanant, Hiroki Shirato, Keiji Kobashi, Sira Sriswasdi

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

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 11%
Researcher 1 11%
Lecturer 1 11%
Student > Master 1 11%
Unknown 5 56%
Readers by discipline Count As %
Medicine and Dentistry 3 33%
Nursing and Health Professions 1 11%
Unknown 5 56%
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 December 2022.
All research outputs
#19,400,321
of 23,868,920 outputs
Outputs from Radiation Oncology
#1,452
of 2,131 outputs
Outputs of similar age
#321,047
of 445,526 outputs
Outputs of similar age from Radiation Oncology
#25
of 34 outputs
Altmetric has tracked 23,868,920 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 2,131 research outputs from this source. They receive a mean Attention Score of 2.9. This one is in the 18th percentile – i.e., 18% 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 445,526 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.