Elimination of Local Abnormal Ventricular Activities
Bibliographic record
Abstract
BACKGROUND: Catheter ablation of ventricular tachycardia (VT) is effective and particularly useful in patients with frequent defibrillator interventions. Various substrate modification techniques have been described for unmappable or hemodynamically intolerable VT. Noninducibility is the most frequently used end point but is associated with significant limitations, so the optimal end point remains unclear. We hypothesized that elimination of local abnormal ventricular activities (LAVAs) during sinus rhythm or ventricular pacing would be a useful and effective end point for substrate-based VT ablation. As an adjunct to this strategy, we used a new high-density mapping catheter and frequently used epicardial mapping. METHODS AND RESULTS: Seventy patients (age, 67±11 years; 7 female) with VT and structurally abnormal ventricle(s) were prospectively enrolled. Conventional mapping was performed in sinus rhythm in all, and a high-density Pentaray mapping catheter was used in the endocardium (n=35) and epicardially. LAVAs were recorded in 67 patients (95.7%; 95% confidence interval, 89.2-98.9). Catheter ablation was performed targeting LAVA with an irrigated-tip catheter placed endocardially via a transseptal or retrograde aortic approach or epicardially via the subxiphoid approach. LAVAs were successfully abolished or dissociated in 47 of 67 patients (70.1%; 95% confidence interval, 58.7-80.1). In multivariate analysis, LAVA elimination was independently associated with a reduction in recurrent VT or death (hazard ratio, 0.49; 95% confidence interval, 0.26-0.95; P=0.035) during long-term follow-up (median, 22 months). CONCLUSIONS: LAVAs can be identified in most patients with scar-related VT. Elimination of LAVAs is feasible and safe and is associated with superior survival free 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".