Mapping the transmural scar and activation for patients with ventricular arrhythmia
Bibliographic record
Abstract
Myocardial scar is the most common substrate for malignant arrhythmia and cardiac arrest. Radiofrequency ablation, as one of the emerging mainstream therapies, currently relies on electrophysiologic (EP) map acquired on endocardial and occasionally epicardial surfaces. As myocardial scar is often complex with shapes varying with the depth of the myocardium, endocardial and epicardial maps may differ substantially, and may fail to identify mid-wall fibrosis that exist in ∼ 30% of patients with nonischemic cardiomyopathy. Alternative image-based delineation of anatomical scar is noninvasive and transmural, but it does not reflect the possibly EP functional anomaly. In this paper, we present a validation study of a previously developed method that combines body-surface electrocardiographic data and image-derived anatomic data to compute EP and scar details along the depth of the myocardium. Experiments were performed on 4 patients referred for ablation associated with myocardial infarction, with gold standards of substrate voltage maps and activation maps acquired by CARTO electroanatomic mapping system. This study exhibits the ability of the presented method in accurately quantifying the scar substrate and capturing abnormal EP patterns, not only on the heart surfaces but along the transmural dimension.
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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) | 0.000 | 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".