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Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab

Overview of attention for article published in Journal of Clinical Medicine, August 2022
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

blogs
1 blog
twitter
10 X users
facebook
1 Facebook page

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
21 Mendeley
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Title
Using Interpretable Machine Learning to Identify Baseline Predictive Factors of Remission and Drug Durability in Crohn’s Disease Patients on Ustekinumab
Published in
Journal of Clinical Medicine, August 2022
DOI 10.3390/jcm11154518
Pubmed ID
Authors

María Chaparro, Iria Baston-Rey, Estela Fernández Salgado, Javier González García, Laura Ramos, María Teresa Diz-Lois Palomares, Federico Argüelles-Arias, Eva Iglesias Flores, Mercedes Cabello, Saioa Rubio Iturria, Andrea Núñez Ortiz, Mara Charro, Daniel Ginard, Carmen Dueñas Sadornil, Olga Merino Ochoa, David Busquets, Eduardo Iyo, Ana Gutiérrez Casbas, Patricia Ramírez de la Piscina, Marta Maia Boscá-Watts, Maite Arroyo, María José García, Esther Hinojosa, Jordi Gordillo, Pilar Martínez Montiel, Benito Velayos Jiménez, Cristina Quílez Ivorra, Juan María Vázquez Morón, José María Huguet, Yago González-Lama, Ana Isabel Muñagorri Santos, Víctor Manuel Amo, María Dolores Martín Arranz, Fernando Bermejo, Jesús Martínez Cadilla, Cristina Rubín de Célix, Paola Fradejas Salazar, Antonio López San Román, Nuria Jiménez, Santiago García-López, Anna Figuerola, Itxaso Jiménez, Francisco José Martínez Cerezo, Carlos Taxonera, Pilar Varela, Ruth de Francisco, David Monfort, Gema Molina Arriero, Alejandro Hernández-Camba, Francisco Javier García Alonso, Manuel Van Domselaar, Ramón Pajares-Villarroya, Alejandro Núñez, Francisco Rodríguez Moranta, Ignacio Marín-Jiménez, Virginia Robles Alonso, María del Mar Martín Rodríguez, Patricia Camo-Monterde, Iván García Tercero, Mercedes Navarro-Llavat, Lara Arias García, Daniel Hervías Cruz, Sebastian Kloss, Alun Passey, Cynthia Novella, Eugenia Vispo, Manuel Barreiro-de Acosta, Javier P. Gisbert

X Demographics

X Demographics

The data shown below were collected from the profiles of 10 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 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 4 19%
Librarian 3 14%
Researcher 2 10%
Student > Doctoral Student 1 5%
Student > Bachelor 1 5%
Other 5 24%
Unknown 5 24%
Readers by discipline Count As %
Medicine and Dentistry 9 43%
Computer Science 2 10%
Psychology 1 5%
Nursing and Health Professions 1 5%
Chemistry 1 5%
Other 1 5%
Unknown 6 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 2022.
All research outputs
#3,238,510
of 24,900,093 outputs
Outputs from Journal of Clinical Medicine
#1,777
of 14,940 outputs
Outputs of similar age
#66,238
of 424,019 outputs
Outputs of similar age from Journal of Clinical Medicine
#86
of 928 outputs
Altmetric has tracked 24,900,093 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,940 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 88% 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 424,019 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 84% of its contemporaries.
We're also able to compare this research output to 928 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.