Decrement Evoked Potential Mapping
Bibliographic record
Abstract
BACKGROUND: Substrate-based mapping for ventricular tachycardia (VT) ablation is hampered by its inability to determine critical sites of the VT circuit. We hypothesized that those potentials, which delay with a decremental extrastimulus (decrement evoked potentials or DEEPs), are more likely to colocalize with the diastolic pathways of VT circuits. METHODS AND RESULTS: DEEPs were identified in intraoperative left ventricular maps from 6 patients with ischemic cardiomyopathy (total 9 VTs) and were compared with late potential (LP) and activation maps of the diastolic pathway for each VT. Mathematical modeling was also used to further validate and elucidate the mechanisms of DEEP mapping. All patients demonstrated regions of DEEPs and LPs. The mean endocardial surface area of these potentials was 18±4% and 21±6%, respectively (P=0.13). The mean sensitivity for identifying the diastolic pathway in VT was 50±23% for DEEPs and 36±32% for LPs (P=0.31). The mean specificity was 43±23% versus 20±8% for DEEP and LP mapping, respectively (P=0.031). The electrograms that displayed the greatest decrement in each case had a sensitivity and specificity for the VT isthmus of 29±10% and 95±1%, respectively. Mathematical modeling studies recapitulated DEEPs at the VT isthmus and demonstrated their role in VT initiation with a critical degree of decrement. CONCLUSIONS: In this preliminary study, DEEP mapping was more specific than LP mapping for identifying the critical targets of VT ablation. The mechanism of DEEPs relates to conduction velocity restitution magnified by zigzag conduction within scar channels.
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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 | 0.002 |
| 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".