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Record W3014511103 · doi:10.1002/ejhf.1815

The Electrocardiogram in the Diagnosis and Management of Patients with Dilated Cardiomyopathy

2020· review· en· W3014511103 on OpenAlexaff
Gherardo Finocchiaro, Marco Merlo, Nabeel Sheikh, Giulia De Angelis, Michael Papadakis, Iacopo Olivotto, Claudio Rapezzi, Gerald Carr‐White, Sanjay Sharma, Luisa Mestroni, Gianfranco Sinagra

Bibliographic record

VenueEuropean Journal of Heart Failure · 2020
Typereview
Languageen
FieldMedicine
TopicCardiomyopathy and Myosin Studies
Canadian institutionsSt. Thomas Hospital
FundersMinistero della SaluteEnte Cassa di Risparmio di FirenzeEuropean Commission
KeywordsMedicineDilated cardiomyopathyCardiologyInternal medicineHeart failureCoronary artery diseaseAmbulatory ECGDiseaseHypertrophic cardiomyopathyElectrocardiography

Abstract

fetched live from OpenAlex

The term dilated cardiomyopathy (DCM) defines a heterogeneous group of cardiac disorders, which are characterized by left ventricular or biventricular dilatation and systolic dysfunction in the absence of abnormal loading conditions or coronary artery disease sufficient to cause global systolic impairment. In approximately one third of cases, DCM is familial with a genetic pathogenesis and various patterns of inheritance. Although the electrocardiogram (ECG) has been considered traditionally non-specific in DCM, the recently acquired knowledge of the genotype-phenotype correlations provides novel opportunities to identify patterns and abnormalities that may point toward specific DCM subtypes. A learned ECG interpretation in combination with an appropriate use of other ECG-based techniques including ambulatory ECG monitoring, exercise tolerance test and imaging modalities, such as echocardiography and cardiovascular magnetic resonance, may allow the early identification of specific genetic or acquired forms of DCM. Furthermore, ECG abnormalities may reflect the severity of the disease and provide a useful tool in risk stratification and management. In the present review, we discuss the current role of the ECG in the diagnosis and management of DCM. We describe various clinical settings where the appropriate use and interpretation of the ECG can provide invaluable clues, contributing to the important role of this basic tool as cardiovascular medicine evolves.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.949
Threshold uncertainty score0.540

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.264
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations103
Published2020
Admission routes1
Has abstractyes

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