Numerical computations of cardiac AP using level set based geometries
Bibliographic record
Abstract
This article proposes two avenues to help improve the realism of numerical computations for cardiac electrophysiology while maintaining manageable computational resources. We first propose an asymptotic analysis to adjust the parameters and use a simple two-variable ionic model to reproduce the main characteristics of the cardiac action potential (AP) in various myocardial regions. This ionic model is embedded in the bidomain model that is used to propagate the AP in the heart. Our second contribution is a finite element method that couples the heart with the torso in a single variational formulation and allows non body fitted meshes at the interface between the myocardium and the torso/ventricle cavities. This interface is described through a level-set function obtained from the segmentation of patient medical images. Using a 2D test case, we compare the use of body-fitted and non body-fitted meshes and analyze the impact of both approaches on the accuracy of the solutions, including an anisotropic mesh adaptation strategy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".