Robotic magnetic-guided catheter ablation in patients with congenital heart disease: a systematic review and pooled analysis
Bibliographic record
Abstract
BACKGROUND: Robotic magnetic navigation (RMN) has emerged as a potential solution to overcome challenges associated with catheter ablation of arrhythmias in patients with congenital heart disease (CHD). OBJECTIVES: To assess safety and efficacy of RMNguided catheter ablation in patients with CHD. DESIGN AND METHODS: A systematic review and pooled analysis was conducted on patients with CHD who underwent RMNguided catheter ablation. Random effects models were used to generate pooled estimates with the inverse variance method used for weighting studies. RESULTS: Twentyfour nonoverlapping records included 167 patients with CHD, mean age 36.5 years, 44.6% female. Type of CHD was simple in 27 (16.2%), moderate in 32 (19.2%), and complex in 106 (63.5%). A total of 202 procedures targeted 260 arrhythmias, the most common being macroreentrant atrial circuits. The mean procedural duration was 207.5 minutes, with a mean fluoroscopy time of 12.1 minutes. The pooled acute success rate was 89.2% [95% CI (77.8%, 97.4%)]. Freedom from arrhythmia recurrence was 84.5% [95% CI (72.5%, 94.0%)] over a mean follow-up of 24.3 months. The procedural complication rate was 3.5% with no complication attributable to RMN technology. CONCLUSION: RMN-guided ablation appears to be safe and effective across a variety of arrhythmia substrates and types of CHD.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".