Decrement Evoked Potential Mapping to Guide Ventricular Tachycardia Ablation: Elucidating the Functional Substrate
Bibliographic record
Abstract
Empirical approaches to targeting the ventricular tachycardia (VT) substrate include mapping of late potentials, local abnormal electrogram, pace-mapping and homogenisation of the abnormal signals. These approaches do not try to differentiate between the passive or active role of local signals as the critical components of the VT circuit. By not considering the functional components, these approaches often view the substrate as a fixed anatomical barrier. Strategies to improve the success of VT ablation need to include the identification of critical functional substrate. Decrement-evoked potential (DeEP) mapping has been developed to elucidate this using an extra-stimulus added to a pacing drive train. With knowledge translation in mind, the authors detail the evolution of the DeEP concept by way of a study of simultaneous panoramic endocardial mapping in VT ablation; an <italic>in silico</italic> modelling study to demonstrate the factors influencing DeEPs; a multicentre VT ablation validation study; a practical approach to DeEP mapping; the potential utility of DeEPs to identify arrhythmogenic atrial substrate; and, finally, other functional mapping strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".