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Machine Learning-Based Prediction Model for ICU Mortality After Continuous Renal Replacement Therapy Initiation in Children

Overview of attention for article published in Critical Care Explorations, December 2024
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

Mentioned by

twitter
6 X users
bluesky
1 Bluesky user

Readers on

mendeley
19 Mendeley
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Article details
Title
Machine Learning-Based Prediction Model for ICU Mortality After Continuous Renal Replacement Therapy Initiation in Children
Published in
Critical Care Explorations, December 2024
DOI 10.1097/cce.0000000000001188
Pubmed ID
Authors

Sameer Thadani, Tzu-Chun Wu, Danny T. Y. Wu, Aadil Kakajiwala, Danielle E. Soranno, Gerard Cortina, Rachana Srivastava, Katja M. Gist, Shina Menon, Emily Ahern, Ayse Akcan Arikan, Issa Alhamoud, Rashid Alobaidi, Pilar Anton-Martin, Shanthi S. Balani, Matthew Barhight, Abby Basalely, Amee M. Bigelow, Gabriella Bottari, Andrea Cappoli, Abhishek Chakraborty, Eileen A. Ciccia, Michaela Collins, Denise Colosimo, Gerard Cortina, Mihaela A. Damian, Sara De la Mata Navazo, Gabrielle DeAbreu, Akash Deep, Kathy L. Ding, Kristin J. Dolan, Elbahlawan, Sarah N. Fernandez Lafever, Dana Y. Fuhrman, Ben Gelbart, Katja M. Gist, Stephen M. Gorga, Francesco Guzzi, Isabella Guzzo, Taiki Haga, Elizabeth Harvey, Denise C. Hasson, Taylor Hill-Horowitz, Haleigh Inthavong, Catherine Joseph, Ahmad Kaddourah, Aadil Kakajiwala, Aaron D. Kessel, Sarah Korn, Kelli A. Krallman, David M. Kwiatkowski, Jasmine Lee, Laurance Lequier, Tina Madani Kia, Kenneth E. Mah, Eleonora Marinari, Susan D. Martin, Shina Menon, Tahagod H. Mohamed, Catherine Morgan, Theresa A. Mottes, Melissa A. Muff-Luett, Siva Namachivayam, Tara M. Neumayr, Jennifer Nhan, Abigail O’Rourke, Nicholas J. Ollberding, Matthew G. Pinto, Dua Qutob, Valeria Raggi, Stephanie Reynaud, Zaccaria Ricci, Zachary A. Rumlow, María J. Santiago Lozano, Emily See, David T. Selewski, Carmela Serpe, Alyssa Serratore, Ananya Shah, Weiwen V. Shih, H. Stella Shin, Cara L. Slagle, Sonia Solomon, Danielle E. Soranno, Rachana Srivastava, Natalja L. Stanski, Michelle C. Starr, Erin K. Stenson, Amy E. Strong, Susan A. Taylor, Sameer V. Thadani, Amanda M. Uber, Brynna Van Wyk, Tennille N. Webb, Huaiyu Zang, Emily E. Zangla, Michael Zappitelli

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Timeline Attention over time Attention Score history
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Activity
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X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Bachelor 2 11%
Professor 2 11%
Lecturer 1 5%
Student > Doctoral Student 1 5%
Student > Master 1 5%
Other 2 11%
Unknown 10 53%
Readers by discipline
Readers by discipline Count As %
Medicine and Dentistry 5 26%
Computer Science 2 11%
Psychology 2 11%
Nursing and Health Professions 1 5%
Unknown 9 47%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 24 March 2025.
All research outputs
#8,510,518
of 29,323,180 outputs
Outputs from Critical Care Explorations
#669
of 1,078 outputs
Outputs of similar age
#100,934
of 360,412 outputs
Outputs of similar age from Critical Care Explorations
#8
of 22 outputs
Altmetric has tracked 29,323,180 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 1,078 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 16.7. This one is in the 37th percentile – i.e., 37% 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 360,412 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.