ArticleJournal of the American Society of Nephrology : JASN2025
Systematic Review and Meta-Analysis of Machine Learning Models for Acute Kidney Injury Risk Classification.
Article in Journal of the American Society of Nephrology : JASN, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
13 citing papers in PubMed.
- Article
- Machine learning models predicting extubation success in mechanically ventilated patients: a systematic review and meta-analysis.Intensive care medicine experimental · 2026Review
- Development and validation of machine learning-based prediction models for adult bowel necrosis for patients presenting with acute abdominal pain at the emergency department: a multicenter retrospective cohort study.BMC medical informatics and decision making · 2026Article
- Association between HALP score and in-hospital outcomes in patients with acute kidney injury: a retrospective cohort study.Scientific reports · 2026Article
- Acute kidney injury over the past decade: from definition evolution to pathogenesis insights and innovative therapeutic strategies.Cellular and molecular life sciences : CMLS · 2026Review
- Artificial Intelligence in Critical Care Nephrology: Current Applications, Emerging Techniques, and Challenges to Clinical Integration.Kidney360 · 2026Review
- Transforming nephrology through artificial intelligence: a state-of-the-art roadmap for clinical integration.Clinical kidney journal · 2026Review
- Contemporary methods for ancient problems: AI for AKI.Intensive care medicine experimental · 2026Article
- Prediction of severe sepsis-associated acute kidney injury incorporating immune-inflammatory profiles: development and validation of a machine learning model in a multicenter prospective cohort study.Frontiers in immunology · 2026Article
- From haemodynamics to kidney risk: AI-based early prediction validated in general and burn ICU populations.European heart journal. Digital health · 2026Article
- Prediction of Imminent Peritoneal Dialysis-Associated Peritonitis Using Time-Updated Electronic Health Records and Machine Learning: A Temporal Validation Study.Journal of inflammation research · 2026Article
- Machine learning studies of drug-induced nephrotoxicity: a scoping review.Therapeutic advances in drug safety · 2026Article
- Artificial Intelligence in Nephrology: From Early Detection to Clinical Management of Kidney Diseases.Bioengineering (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
What Socratic holds
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.