High-Density Mapping in Ventricular Tachycardia Ablation: A PentaRay<sup>®</sup> Study
Bibliographic record
Abstract
Background: High-density mapping of ventricular tachycardia (VT) with PentaRay ® (Biosense-Webster) provides high resolution with discrimination of local abnormal electrograms and slow conducting channels. We evaluate the feasibility of PentaRay ® to characterize the anatomical substrate and assume an influence of the outcome despite limitations. Methods: Over a 24-month period, 26 endocardial and four epicardial maps were obtained of 26 VT patients (18 ischemic cardiomyopathy (ICM, 69.2%) and 8 non-ischemic cardiomyopathy (NICM, 30.8%), age 65 ± 9 years). Catheter ablation (CA) was performed with the aim of transecting the isthmus. The endpoint was non-inducibility of any VT. Manual review of the maps was performed and focused on evaluating scarring, bipolar electrograms, and procedure times. Results: In 55.6 ± 34.4 min, 1,085.9 ± 726.2 points were created. The mean ablation time was 50.8 ± 30.1 min. The endpoint was achieved in 12 patients (46.2%). The mean dense scar area and the mean patchy scar area were 49.4± 51.8 cm 2 (range 0 - 190 cm 2 ) and 14.7 ± 14.9 cm 2 (range 0 - 110 cm 2 ), respectively. Analyzing the learning curve, we found a tendency in decreasing procedure times. During the course of follow-up treatment averaging a 14-month period, device interrogation showed that 17 patients (65.4%) had remained free of any arrhythmia recurrence. Conclusion: The high-density maps with PentaRay ® were safely created in a short period of time. Our manual review of the maps reveals limitations of current annotation criteria; nevertheless, medium-term outcomes were encouraging. Further prospective studies are required to validate our findings in a larger cohort of patients. Cardiol Res. 2017;8(6):293-303 doi: https://doi.org/10.14740/cr636w
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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 | 0.001 |
| 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".