ArticleFrontiers in pediatrics2021
A Machine Learning Model to Predict Intravenous Immunoglobulin-Resistant Kawasaki Disease Patients: A Retrospective Study Based on the Chongqing Population.
Article in Frontiers in pediatrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 28 citations in OpenAlex.
- Quality and performance of machine learning versus logistic regression for predicting IVIG resistance in Kawasaki disease: a PROBAST+AI systematic comparison.BMC medical research methodology · 2026Pooled it
- Machine learning prediction models for intravenous immunoglobulin resistance in Kawasaki disease: a meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Single Nucleotide Polymorphisms and Their Association with Coronary Artery Aneurysms and IVIG Resistance in Kawasaki Disease in Ireland.Pediatric cardiology · 2026Article
- Development and validation of a novel interpretable machine learning model integrating immune-inflammatory indicators for intravenous immunoglobulin resistance in Kawasaki disease.Translational pediatrics · 2026Article
- Preparing Allergists to Practice in 2050 Using Artificial Intelligence.The journal of allergy and clinical immunology. In practice · 2025Review
- An integrated and interpretable machine learning framework for Kawasaki disease diagnosis and risk prediction.Translational pediatrics · 2025Article
- Explainable deep learning algorithm for distinguishing IVIG-Resistant Kawasaki disease in Shandong peninsula, China.BMC pediatrics · 2025Article
- A visualized nomogram to predict intravenous immunoglobulin resistance in Kawasaki disease: a study based on the population in Southern China.Italian journal of pediatrics · 2025Article
- A machine learning-based model to predict intravenous immunoglobulin resistance in Kawasaki disease.iScience · 2025Article
- Applications of Artificial Intelligence in Vasculitides: A Systematic Review.ACR open rheumatology · 2025Review
- Clinical prediction of intravenous immunoglobulin-resistant Kawasaki disease based on interpretable Transformer model.PloS one · 2025Article
- Routine Laboratory Markers-Based Machine Learning Model for Predicting Severe Kawasaki Disease in Pediatric Patients.Journal of inflammation research · 2025Article
- An Update on Kawasaki Disease.Current rheumatology reports · 2024Review
- Application of artificial intelligence in the diagnosis and treatment of Kawasaki disease.World journal of clinical cases · 2024Article
- Construction and validation of predictive models for intravenous immunoglobulin-resistant Kawasaki disease using an interpretable machine learning approach.Clinical and experimental pediatrics · 2024Article
- Prediction Models for Intravenous Immunoglobulin Non-Responders of Kawasaki Disease Using Machine Learning.Clinical drug investigation · 2024Article
- Knowledge framework of intravenous immunoglobulin resistance in the field of Kawasaki disease: A bibliometric analysis (1997-2023).Immunity, inflammation and disease · 2024Article
- Intravenous immunoglobulin resistance in Kawasaki disease patients: prediction using clinical data.Pediatric research · 2024Article
- Predictors and a novel predictive model for intravascular immunoglobulin resistance in Kawasaki disease.Italian journal of pediatrics · 2023Article
- A machine learning model for distinguishing Kawasaki disease from sepsis.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.