Evidence map›Paper›PMID 29593570›Full record

ArticleFrontiers in physiology2018

Distinct ECG Phenotypes Identified in Hypertrophic Cardiomyopathy Using Machine Learning Associate With Arrhythmic Risk Markers.

Aurore Lyon, Rina Ariga, Ana Mincholé, Masliza Mahmod, Elizabeth Ormondroyd, Pablo Laguna, Nando de Freitas, Stefan Neubauer, Hugh Watkins, Blanca Rodriguez

Open access · goldAbstract read
In one paragraph

Article in Frontiers in physiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 2 pooled it
6.5field-weighted citation impact, top 3% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

41 citing papers in PubMed, 2 syntheses or guidelines pooled it, 83 citations in OpenAlex.

  1. Pooled it
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  5. A New Era in the Management of Hypertrophic Cardiomyopathy.Reviews in cardiovascular medicine · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 2 countries.

Aurore LyonDepartment of Computer Science, University of Oxford, Oxford, United Kingdom.
Rina ArigaDivision of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.
Ana MincholéDepartment of Computer Science, University of Oxford, Oxford, United Kingdom.
Masliza MahmodDivision of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.
Elizabeth OrmondroydDivision of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.
Pablo LagunaBiomedical Signal Interpretation & Computational Simulation Group, CIBER-BBN, University of Zaragoza, Zaragoza, Spain.
Nando de FreitasDepartment of Computer Science, University of Oxford, Oxford, United Kingdom.
Stefan NeubauerDivision of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.
Hugh WatkinsDivision of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom.
Blanca RodriguezDepartment of Computer Science, University of Oxford, Oxford, United Kingdom.
University of Oxford · GBBiomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine · ES

Funding

British Heart Foundation FS/12/32/29559National Centre for the Replacement, Refinement and Reduction of Animals in Research NC/P001076/1
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

computational clusteringe-cardiologyelectrocardiographyhypertrophic cardiomyopathyphenotyping

Identifiers

PMID29593570
PMCPMC5859357
OpenAlexW2789374978

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.