ArticleFrontiers in physiology2021
Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data.
Article in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review.Heart failure reviews · 2024Pooled it
- A New Method of Modeling the Multi-stage Decision-Making Process of CRT Using Machine Learning with Uncertainty Quantification.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence in heart failure.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2026Review
- Quest for the ideal assessment of electrical ventricular dyssynchrony in cardiac resynchronization therapy.Journal of molecular and cellular cardiology plus · 2024Review
- A study of machine learning to predict NRDS severity based on lung ultrasound score and clinical indicators.Frontiers in medicine · 2024Article
- Machine learning model of imipenem-resistantHealth science reports · 2023Article
- Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy.Frontiers in physiology · 2023Article
- Primer on Machine Learning in Electrophysiology.Arrhythmia & electrophysiology review · 2023Review
- The saga of dyssynchrony imaging: Are we getting to the point.Frontiers in cardiovascular medicine · 2023Review
- Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias.Frontiers in physiology · 2022Article
- Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death.Frontiers in cardiovascular medicine · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 2 countries.
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.