Atrial decremental evoked potentials accurately determine the critical isthmus of intra-atrial re-entrant tachycardia
Bibliographic record
Abstract
We have shown before, the utility of DEEP mapping in identifying critical isthmus of scar VT. We hypothesized that, as intra-atrial re-entrant tachycardia (IART) substrate is similar to scar VT by virtue of surgical scars, DEEP mapping could be useful in identifying critical targets for ablation. Two patients with corrected transposition of great arteries (cc TGA) and previous cardiac surgery were selected for the study. Both had recurrent symptomatic IART prompting multiple hospital visits. A decapolar coronary sinus catheter was used for both patients. For the first patient a 64-electrode basket array and for the second patient a duo-decapolar catheter was used for mapping. The late potentials and decremental local potentials were annotated on the electro-anatomical map (CARTO, Biosense Webster, Israel). In sinus rhythm, a pacing train was applied from the CS proximal electrode, and extra stimulus was introduced. The site with maximum local electrogram decrement was considered the critical isthmus and was ablated. The arrhythmia was non-inducible after ablation at this DEEP site for both patients. DEEP mapping was useful in localizing the critical ablation target in IART. The full-length version of this report can be viewed at: https://www.escardio.org/Education/E-Learning/Clinical-cases/Electrophysiology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".