ArticleInternational journal of environmental research and public health2023
Machine Learning Models to Predict the Risk of Rapidly Progressive Kidney Disease and the Need for Nephrology Referral in Adult Patients with Type 2 Diabetes.
Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Clinical Artificial Intelligence Agents in Nephrology: From Prediction to Action Through Workflow-Native Intelligence-A Roadmap for Workflow-Integrated Care.Journal of clinical medicine · 2026Review
- Explainable Artificial Intelligence for Early Diabetic Kidney Disease Risk Profiling in Type 2 Diabetes: Translational Readiness and Clinical Boundaries.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Review
- Machine learning models to predict osteoporosis in patients with chronic kidney disease stage 3-5 and end-stage kidney disease.Scientific reports · 2025Article
- Precision Medicine Approaches to Diabetic Kidney Disease: Personalized Interventions on the Horizon.Cureus · 2023Review
- Article
- Predictive role of serum C-peptide in new-onset renal dysfunction in type 2 diabetes: a longitudinal observational study.Frontiers in endocrinology · 2023Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early detection of rapidly progressive kidney disease is key to improving the renal outcome and reducing complications in adult patients with type 2 diabetes mellitus (T2DM). We aimed to construct a 6-month machine learning (ML) predictive model for the risk of rapidly progressive kidney disease and the need for nephrology referral in adult patients with T2DM and an initial estimated glomerular filtration rate (eGFR) ≥ 60 mL/min/1.73 m
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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 Socratic graph.