ArticleJournal of endourology2024
A Novel Machine-Learning Algorithm to Predict Stone Recurrence with 24-Hour Urine Data.
Article in Journal of endourology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
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Who cites it
8 citing papers in PubMed.
- Evaluation of explainable machine learning models for predicting mid-term stone recurrence after percutaneous nephrolithotomy: a retrospective observational cohort study.International urology and nephrology · 2026Observational
- Construction of a LASSO regression-based predictive model for recurrence of urinary tract stones.Translational andrology and urology · 2026Article
- Identifying recurrent stone formers with machine learning: A single-centre observational study.BJUI compass · 2026Article
- Construction and validation of a urinary stone composition prediction model based on machine learning.Urolithiasis · 2025Article
- Use of Artificial Intelligence Methods for Improved Diagnosis of Urinary Tract Infections and Urinary Stone Disease.Journal of clinical medicine · 2025Review
- Development and validation of a machine learning-based prediction model for urinary calculi recurrence.Urolithiasis · 2025Article
- AI-Driven Prediction of Renal Stone Recurrence Following ECIRS: A Machine Learning Approach to Postoperative Risk Stratification Incorporating 24-Hour Urine Data.Journal of clinical medicine · 2025Article
- Predictive value of the stone-free rate after percutaneous nephrolithotomy based on multiple machine learning models.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
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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.