Clinical research: remote magnetic navigation vs. manually controlled catheter ablation of right ventricular outflow tract arrhythmias: a retrospective study
Bibliographic record
Abstract
Aims: Remote magnetic navigation (RMN) is an alternative to manual catheter control (MCC) radiofrequency ablation of right ventricular outflow tract (RVOT) arrhythmias. The data to support RMN approach is limited. We aimed to investigate the clinical and procedural outcomes in a cohort of patients undergoing RVOT premature ventricular complex/ventricular tachycardia (PVCs/VT) ablation procedures using RMN vs. MCC. Methods and results: Data was collected from two centres. Eighty-nine consecutive RVOT PVCs/VT ablation procedures were performed in 75 patients; RMN: 42 procedures and MCC: 47 procedures. CARTOXPTM or CARTO3 (Biosense Webster) was used for endocardial mapping in 19/42 (45%) in RMN group and 28/47 (60%) in MCC group; EnSiteTM NavXTM (St. Jude Medical) was used in the rest of the cohort. Stereotaxis platform (Stereotaxis Inc., St. Louis, MO, USA) was used for RMN approach. Procedural time was 113 ± 53 min in the RMN group and 115 ± 69 min in MCC (P = 0.90). Total fluoroscopic time was 10.9 ± 5.8 vs. 20.5 ± 13.8 (P < 0.05) and total ablation energy application time 7.0 ± 4.7 vs 11.9 ± 16 (P = 0.67) accordingly. There were two complications in RMN group and five in MCC (P = 0.43). Acute procedural success rate was 80% in RMN vs. 74% in MCC group (P = 0.46). After a median follow-up of 25 months (interquartile range 13-34), the success rate remained 55% in the RMN group and 53% in MCC (P = 0.96). Conclusion: Right ventricular outflow tract arrhythmia ablations were performed using half of fluoroscopic times with Stereotaxis platform RMN compared to manual approach. Acute and chronic success rates as well as complication rates were not significantly different.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".