Meta‐Analysis to Assess the Appropriate Endpoint for Slow Pathway Ablation of Atrioventricular Nodal Reentrant Tachycardia
Bibliographic record
Abstract
BACKGROUND: There are little data on the appropriate endpoint for slow pathway ablation that balances acceptable procedural times, recurrence rates, and complication rates. This study compared recurrence rates of three commonly utilized endpoints of slow pathway ablation for atrioventricular nodal reentrant tachycardia (AVNRT). METHODS: We performed a meta-analysis of AVNRT slow pathway ablation cohorts by searching electronic databases, the Internet, and conference proceedings. Inclusion criteria were age >18 years, >20 human subjects per study, primary AVNRT ablation, English language publication, and >1 month of follow-up. Data were analyzed with a fixed-effects model using Comprehensive Meta-Analysis software version 2.2.046 (Biostat, Englewood, NJ, USA). RESULTS: We included 10 studies encompassing 1,204 patients with a mean age of 41-53 years. Endpoints were complete slow pathway ablation, residual jump only, and single remaining echo beat. Pooled estimates revealed 28 of 641 patients (4.4%) with complete slow pathway ablation, 13 of 192 patients (6.8%) with a residual jump only, and 24 of 371 patients (6.5%) with one echo had recurrences. With uniform isoproterenol use after ablation, there was no significant difference in recurrence rates among the endpoints. However, when isoproterenol was utilized after ablation only if needed to induce AVNRT before ablation, a significantly higher recurrence rate occurred in patients with a residual jump (P = 0.002), a single echo (P = 0.003), or the combined group of a residual jump and/or one echo (P = 0.001). CONCLUSIONS: Isoproterenol should be used routinely after slow pathway modification, when a residual jump and/or single echo remain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".