Intramural Ventricular Arrhythmias: How to Crack a Hard Nut
Bibliographic record
Abstract
PURPOSE OF THE REVIEW: Successful catheter ablation of ventricular arrhythmias depends on identifying the critical tissues that sustain the arrhythmia. Increasingly, the intramural space is being recognized as an important source of idiopathic and reentrant ventricular arrhythmias, representing a common cause of ablation failure. A systematic approach to mapping and ablating these arrhythmias is key to optimize outcomes. RECENT FINDINGS: Intramural ventricular arrhythmias are common in certain anatomical locations such as the left ventricular ostium or the interventricular septum. In these cases, mapping of the septal coronary veins provides an opportunity to explore the intramural compartment of the septum to perform activation mapping, entrainment and/or pace mapping. When an intramural arrhythmia is identified, ablation may require radiofrequency application from multiple sites, prolonged lesions, or special ablation techniques such as bipolar ablation or transvenous ethanol injection. Identification of intramural ventricular arrhythmias depends on comprehensive mapping that should include the coronary venous system, and ablation often requires advanced techniques. This paper provides a guide on when to suspect an intramural ventricular arrhythmia in the electrophysiology laboratory and how to approach mapping and ablation in these challenging cases.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".