ArticlePediatric nephrology (Berlin, Germany)2018
Beyond playing games: nephrologist vs machine in pediatric dialysis prescribing.
Article in Pediatric nephrology (Berlin, Germany), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Effect of Remote and Virtual Technology on Home Dialysis.Clinical journal of the American Society of Nephrology : CJASN · 2024Review
- Bedside sonographic assessments for predicting predialysis fluid overload in children with end-stage kidney disease.European journal of pediatrics · 2021Article
- Effect of hemodialysis on impedance cardiography (electrical velocimetry) parameters in children.Pediatric nephrology (Berlin, Germany) · 2020Observational
- Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review.BioMed research international · 2020Review
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In a recent article in Pediatric Nephrology, Olivier Niel and colleagues applied an artificial intelligence algorithm to a clinical problem that continues to challenge experienced pediatric nephrologists: optimizing the target weight of children on dialysis. They compared blood pressure, antihypertensive medication and intradialytic symptoms in children whose target weight was prescribed firstly by a nephrologist, then subsequently using a machine learning algorithm. Improvements in all outcome measures are reported. Their innovative approach to tackling this important clinical problem appears promising. In this editorial, we discuss the strengths and weaknesses of their study and consider to what extent machine learning strategies are suited to optimizing pediatric dialysis outcomes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.