Early vs. deferred catheter ablation of ventricular tachycardia in patients of ischaemic substrate: systematic review and meta-analysis of clinical outcomes
Bibliographic record
Abstract
Aims: Ventricular tachycardia (VT) ablation has been shown to reduce the recurrence of VT episodes, but the timing of performing VT ablation (early; at the time of implantable cardioverter defibrillator implantation) or (deferred: after the patient has received ICD shocks) remains controversial. The objective is to conduct a systematic review and meta-analysis of published data from randomized controlled trials (RCTs) in patients with ischaemic cardiomyopathy (ICM) with the aim of comparing outcome of VT ablation stratified by procedural timing. Methods and results: = 0.01). For the secondary outcomes, we observed that an earlier timing of VT ablation was also associated with both a decrease in cardiac mortality (pooled OR of 0.59, 95% CI of 0.43-0.82) and in the subsequent risk of VT storm (pooled OR of 0.63, 95% CI of 0.51-0.78) when compared with a deferred timing. The cumulative ARR for cardiac mortality was 0.07 and NNT was 15. Conclusion: The findings from this pooled analysis of seven major RCTs suggest that performing early VT ablation may be beneficial in reducing recurrent VT, ICD shocks, and electrical storm and could also improve cardiac mortality. The benefit of performing early VT ablation was greater in patients with LVEF of >30% amongst this ICM cohort.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".