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Record W2326957496 · doi:10.1097/hco.0000000000000035

Ventricular arrhythmias in patients with heart failure secondary to reduced ejection fraction

2013· review· en· W2326957496 on OpenAlexaff
Girish M. Nair, Pablo B. Nery, Calum J. Redpath, David H. Birnie

Bibliographic record

VenueCurrent Opinion in Cardiology · 2013
Typereview
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineCardiologyInternal medicineEjection fractionAmiodaroneHeart failureCatheter ablationVentricular tachycardiaIschemic cardiomyopathyCardiac resynchronization therapyImplantable cardioverter-defibrillatorDilated cardiomyopathyCardiomyopathyAblationAtrial fibrillation

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: To review the management of ventricular arrhythmias in patients with heart failure secondary to reduced ejection fraction (HFrEF). RECENT FINDINGS: Recurrent ventricular arrhythmias and automatic implantable cardioverter defibrillator (AICD) shocks are responsible for significant mortality and morbidity in patients with HFrEF. Antiarrhythmic drugs and catheter ablation are the main treatment options. Frequent premature ventricular contractions (PVCs; >10,000-20,000/24-h period) are being recognized as a cause of cardiomyopathy and suboptimal response to cardiac resynchronization therapy (CRT). Patients with ventricular assist devices (VADs) have frequent ventricular tachyarrhythmias resulting in increased morbidity and mortality. Such patients may need continuation of active ICD therapy and adjunctive catheter ablation. SUMMARY: There is a pressing need to develop new antiarrhythmic drugs to treat patients with recurrent AICD shocks. The effectiveness of catheter ablation as first-line therapy for preventing ventricular arrhythmias and recurrent AICD shocks needs to be directly compared with amiodarone. Ventricular tachyarrhythmias are common in CRT patients and patients with VADs. Frequent PVCs may result in a reversible form of HFrEF.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.793
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
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 head0.054
GPT teacher head0.361
Teacher spread0.308 · 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.

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

Citations6
Published2013
Admission routes1
Has abstractyes

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