Ventricular tachycardia ablation in ischaemic cardiomyopathy: who, when and how?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".