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Prediction of long-term mortality by using machine learning models in Chinese patients with connective tissue disease-associated interstitial lung disease

Overview of attention for article published in Respiratory Research, January 2022
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Mentioned by

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2 X users

Citations

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9 Dimensions

Readers on

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16 Mendeley
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Title
Prediction of long-term mortality by using machine learning models in Chinese patients with connective tissue disease-associated interstitial lung disease
Published in
Respiratory Research, January 2022
DOI 10.1186/s12931-022-01925-x
Pubmed ID
Authors

Di Sun, Yu Wang, Qing Liu, Tingting Wang, Pengfei Li, Tianci Jiang, Lingling Dai, Liuqun Jia, Wenjing Zhao, Zhe Cheng

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

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 13%
Student > Doctoral Student 2 13%
Librarian 1 6%
Researcher 1 6%
Unknown 10 63%
Readers by discipline Count As %
Medicine and Dentistry 3 19%
Agricultural and Biological Sciences 1 6%
Environmental Science 1 6%
Unknown 11 69%
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 10 January 2022.
All research outputs
#19,962,154
of 25,394,764 outputs
Outputs from Respiratory Research
#2,510
of 3,064 outputs
Outputs of similar age
#368,300
of 515,421 outputs
Outputs of similar age from Respiratory Research
#37
of 52 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,064 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.9. This one is in the 11th percentile – i.e., 11% 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 515,421 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.