Evaluation of experimental myocardial infarction models via electromechanical mapping and magnetic resonance imaging
Bibliographic record
Abstract
The diagnostic characteristics of electromechanical mapping (EMM) were evaluated in porcine myocardial infarction (MI) models with the parallel application of cardiac magnetic resonance imaging (cMRI) from the aspect of different pathophysiology and localization. Balloon occlusion in the left anterior descending coronary artery (LAD balloon group) or coil deployment in the LAD (LAD coil group) or circumflex artery (Cx coil group) was applied percutaneously in 16 domestic pigs. Regional left ventricular viability data were captured via cMRI and EMM. The unipolar voltage (UV) value was significantly decreased in segments containing transmural and subendocardial late enhancement compared with viable segments in the LAD balloon, LAD coil, and Cx coil groups. Receiver operating characteristic analysis revealed area under the curve values of 0.809 and 0.691 in the LAD infarct territory, and 0.864 and 0.855 in the Cx infarct territory for the UV compared with cMRI viability results as transmural late enhancement or viable tissue and subendocardial late enhancement or viable tissue, respectively. In conclusion, the UV value detected the presence of scar tissue with differential transmural extent and which represented proper diagnostic features both in the reperfused and nonreperfused models. This data could provide additional benefit in the clinical use of EMM for diagnostic purposes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".