The Feasibility of Endocardial Propagation Mapping Using Magnetic Resonance Guidance in a Swine Model, and Comparison With Standard Electroanatomic Mapping
Bibliographic record
Abstract
The introduction of electroanatomic mapping (EAM) has improved the understanding of the substrate of ventricular tachycardia. EAM systems are used to delineate scar regions responsible for the arrhythmia by creating voltage or activation time maps. Previous studies have identified the benefits of creating MR-guided voltage maps; however, in some cases voltage maps may not identify regions of slow propagation that can cause the reentrant tachycardia. In this study, we obtained local activation time maps and analyzed propagation properties by performing MR-guided mapping of the porcine left ventricle while pacing from the right ventricle. Anatomical and myocardial late gadolinium enhancement images were used for catheter navigation and identification of scar regions. Our MR-guided mapping procedure showed qualitative correspondence to conventional clinical EAM systems in healthy pigs and demonstrated altered propagation in endocardial infarct models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".