ArticleJournal of the American Heart Association2015
Non-ST-Segment Elevation Myocardial Infarction: A Novel and Robust Approach for Early Detection of Patients at Risk.
Article in Journal of the American Heart Association, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- A Wearable Infrared Sensor for Detecting Non-ST Segment Elevation Acute Coronary Syndromes.Life (Basel, Switzerland) · 2026Article
- Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction.Nature medicine · 2023Observational
- Machine Learning for the ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction at First Medical Contact.Research square · 2023Article
- Very low risk of ST-elevation and non-ST-elevation myocardial infarction in patients with chest trauma.American journal of cardiovascular disease · 2023Article
- Incorporation of Serial 12-Lead Electrocardiogram With Machine Learning to Augment the Out-of-Hospital Diagnosis of Non-ST Elevation Acute Coronary Syndrome.Annals of emergency medicine · 2023Observational
- In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.Journal of the American Heart Association · 2021Observational
- Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram.Nature communications · 2020Article
- Emerging ECG methods for acute coronary syndrome detection: Recommendations & future opportunities.Journal of electrocardiologyArticle
- Novel ECG features and machine learning to optimize culprit lesion detection in patients with suspected acute coronary syndrome.Journal of electrocardiologyObservational
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Authors and funding
1 author.
Funding
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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.