ArticleFrontiers in cardiovascular medicine2021
A Risk-Stratification Machine Learning Framework for the Prediction of Coronary Artery Disease Severity: Insights From the GESS Trial.
Article in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Integrating conformal prediction with machine learning for uncertainty-aware risk stratification in coronary artery disease.Frontiers in cardiovascular medicine · 2026Article
- Review
- O2 supplementation disambiguation in clinical narratives to support retrospective COVID-19 studies.BMC medical informatics and decision making · 2024Article
- Computational Cardiology: The Door to the Future of Interventional Cardiology.JACC. Advances · 2023Article
- Prognostic Implications of Clinical, Laboratory and Echocardiographic Biomarkers in Patients with Acute Myocardial Infarction-Rationale and Design of the ''CLEAR-AMI Study''.Journal of clinical medicine · 2023Article
- Machine learning approaches that use clinical, laboratory, and electrocardiogram data enhance the prediction of obstructive coronary artery disease.Scientific reports · 2023Article
- Review
- A Machine Learning Framework for Diagnosing and Predicting the Severity of Coronary Artery Disease.Reviews in cardiovascular medicine · 2023Article
- Single Nucleotide Polymorphisms' Causal Structure Robustness within Coronary Artery Disease Patients.Biology · 2023Article
- Association of clinical, laboratory and imaging biomarkers with the occurrence of acute myocardial infarction in patients without standard modifiable risk factors - rationale and design of the "Beyond-SMuRFs Study".BMC cardiovascular disorders · 2023Article
- Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial.Metabolites · 2022Article
- Cardiac Complications: The Understudied Aspect of Cancer Cachexia.Cardiovascular toxicology · 2022Review
- Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our study aims to develop a data-driven framework utilizing heterogenous electronic medical and clinical records and advanced Machine Learning (ML) approaches for: (
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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.