Patient-Specific Study of Post-Ischemic Cardiac Ventricular Remodeling: A Passive Simulation of Structural Changes in Myofiber Orientation and Stiffness
Bibliographic record
Abstract
Despite recent advances in the computational modeling of cardiovascular diseases and therapies, the effect of post-ischemic remodeling has not been thoroughly studied while considering the unloaded ventricles. Further evaluation is, therefore, needed in order to better understand the effect of alterations in myocardial structure. Herein, we have developed a patient-specific computational model of ischemic cardiomyopathy to assess the influence of microstructure and material change on passive ventricular mechanics. The biventricular geometry has been built and unloaded based on cardiac magnetic resonance (CMR) images of a 64-year-old male patient at end-diastole (ED). Different fiber orientations and material scales were assumed for the model. Results indicated that although some fiber structures produce similar end-diastolic pressure-volume relationships (EDPVRs), differences in initial stress-free shapes and strain patterns determine the subsequent damage to the myocardium. Moreover, stiffening the healthy region means lower myofiber strain. However, material change in the ischemic areas of the ventricles does not alter the passive fiber strain considerably. Such evaluations are required when choosing optimal therapies to alleviate the adverse effects of ischemic cardiomyopathy.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".