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Record W4414909532 · doi:10.1136/heartjnl-2024-325370

Ventricular tachycardia ablation in ischaemic cardiomyopathy: who, when and how?

2025· review· en· W4414909532 on OpenAlexaff
Alexios Hadjis, Corrado De Marco, Jean‐Marc Raymond, John L. Sapp

Bibliographic record

VenueHeart · 2025
Typereview
Languageen
FieldMedicine
TopicCardiac Arrhythmias and Treatments
Canadian institutionsQueen Elizabeth II Health Sciences CentreDalhousie UniversityCentre Hospitalier de l’Université de MontréalHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsCatheter ablationAblationVentricular tachycardiaTachycardiaCardiomyopathyAdverse effectClinical trialElectrocardiography

Abstract

fetched live from OpenAlex

Ventricular tachycardia (VT) is an abnormal rapid heart rhythm that most commonly occurs in the setting of ventricular scar. In patients with ischaemic cardiomyopathy and VT, the most common mechanism is re-entry of electrical activation through narrow channels of diseased myocardium manifesting on the ECG as a regular sustained wide-complex tachycardia that can present clinically with sudden cardiac death (SCD).Implantable cardioverter-defibrillators (ICDs) are proven to reduce the risk of SCD, but do not prevent VT; they treat it when it occurs. Although antiarrhythmic drug therapy has a long history of use to suppress VT, recurrence rates remain high and adverse effects are not negligible. Significant advances have been made over the past decades in catheter-based techniques for VT suppression. Improvements in both mapping accuracy and ablation efficacy have resulted in recent studies demonstrating improved outcomes of catheter ablation of VT. Patient selection for a procedural approach will be important for achieving optimal clinical outcomes.This review provides a comprehensive overview of randomised trials of catheter ablation for VT as well as contemporary VT ablation techniques, and aims to understand which patients should undergo VT ablation, when is the ideal timing for intervention, and how best to achieve freedom from recurrent VT.

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 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 score0.969
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.311
Teacher spread0.290 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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