Distribution of abnormal potentials in chronic myocardial infarction using a real time magnetic resonance guided electrophysiology system
Bibliographic record
Abstract
BACKGROUND: Identification of viable slow conduction zones manifested by abnormal local potentials is integral to catheter ablation of ventricular tachycardia (VT) sites. The relationship between contrast patterns in cardiovascular magnetic resonance (CMR) and local electrical mapping is not well characterized. The purpose of this study was to identify regions of isolated, late and fractionated diastolic potentials in sinus rhythm and controlled-paced rhythm in post-infarct animals relative to regions detected by late gadolinium enhancement CMR (LGE-CMR). METHODS: Using a real-time MR-guided electrophysiology system, electrogram (EGM) recordings were used to generate endocardial electroanatomical maps in 6 animals. LGE-CMR was also performed and tissue classification (dense infarct, gray zone and healthy myocardium) was then correlated to locations of abnormal potentials. RESULTS: For abnormal potentials in sinus rhythm, relative occurrence was equivalent 24%, 27% and 22% in dense scar, gray zone and healthy tissue respectively (p = NS); in paced rhythm, the relative occurrence of abnormal potentials was found to be different with 30%, 42% and 21% in dense scar, gray zone and healthy myocardium respectively (p = 0.001). For location of potentials, in the paced case, the relative frequency of abnormal EGMs was 19.9%, 65.4% and 14.7% in the entry, central pathway and exit respectively (p = 0.05), putative regions being defined by activation times. CONCLUSIONS: Our data suggests that gray zone quantified by LGE-CMR exhibits abnormal potentials more frequently than in healthy tissue or dense infarct when right ventricular apex pacing is used.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".