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Discriminant analysis and machine learning approach for evaluating and improving the performance of immunohistochemical algorithms for COO classification of DLBCL

Overview of attention for article published in Journal of Translational Medicine, June 2019
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)

Mentioned by

twitter
12 tweeters

Citations

dimensions_citation
5 Dimensions

Readers on

mendeley
37 Mendeley
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Title
Discriminant analysis and machine learning approach for evaluating and improving the performance of immunohistochemical algorithms for COO classification of DLBCL
Published in
Journal of Translational Medicine, June 2019
DOI 10.1186/s12967-019-1951-y
Pubmed ID
Authors

Yocanxóchitl Perfecto-Avalos, Alejandro Garcia-Gonzalez, Ana Hernandez-Reynoso, Gildardo Sánchez-Ante, Carlos Ortiz-Hidalgo, Sean-Patrick Scott, Rita Q. Fuentes-Aguilar, Ricardo Diaz-Dominguez, Grettel León-Martínez, Verónica Velasco-Vales, Mara A. Cárdenas-Escudero, José A. Hernández-Hernández, Arturo Santos, José R. Borbolla-Escoboza, Luis Villela

Twitter Demographics

The data shown below were collected from the profiles of 12 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 16%
Professor 5 14%
Researcher 5 14%
Student > Master 4 11%
Other 3 8%
Other 5 14%
Unknown 9 24%
Readers by discipline Count As %
Medicine and Dentistry 6 16%
Engineering 5 14%
Biochemistry, Genetics and Molecular Biology 5 14%
Computer Science 4 11%
Agricultural and Biological Sciences 3 8%
Other 5 14%
Unknown 9 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 29 November 2021.
All research outputs
#4,082,984
of 22,579,714 outputs
Outputs from Journal of Translational Medicine
#647
of 3,930 outputs
Outputs of similar age
#72,650
of 292,348 outputs
Outputs of similar age from Journal of Translational Medicine
#1
of 1 outputs
Altmetric has tracked 22,579,714 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,930 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.3. This one has done well, scoring higher than 83% of its peers.
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 292,348 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them