Trigger and Substrate Mapping and Ablation for Ventricular Fibrillation in the Structurally Normal Heart
Bibliographic record
Abstract
Sudden cardiac death (SCD) represents approximately 50% of all cardiovascular mortality in the United States. The majority of SCD occurs in individuals with structural heart disease; however, around 5% of individuals have no identifiable cause on autopsy. This proportion is even higher in those <40 years old, where SCD is particularly devastating. Ventricular fibrillation (VF) is often the terminal rhythm leading to SCD. Catheter ablation for VF has emerged as an effective tool to alter the natural history of this disease among high-risk individuals. Important advances have been made in the identification of several mechanisms involved in the initiation and maintenance of VF. Targeting the triggers of VF as well as the underlying substrate that perpetuates these lethal arrhythmias has the potential to eliminate further episodes. Although important gaps remain in our understanding of VF, catheter ablation has become an important option for individuals with refractory arrhythmias. This review outlines a contemporary approach to the mapping and ablation of VF in the structurally normal heart, specifically focusing on the following major conditions: idiopathic ventricular fibrillation, short-coupled ventricular fibrillation, and the J-wave syndromes-Brugada syndrome and early-repolarization syndrome.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".