Electrophysiological Cardiac Modeling: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cardiac electrophysiological modeling in conjunction with experimental and clinical findings has contributed to better understanding of electrophysiological phenomena in various species. As our knowledge on underlying electrical, mechanical, and chemical processes has improved over time, mathematical models of the cardiac electrophysiology have become more realistic and detailed. These models have provided a testbed for various hypotheses and conditions that may not be easy to implement experimentally. In addition to the limitations in experimentally validating various scenarios implemented by the models, one of the major obstacles for these models is computational complexity. However, the ever-increasing computational power of supercomputers facilitates the clinical application of cardiac electrophysiological models. The potential clinical applications include testing and predicting effects of pharmaceutical agents and performing patient-specific ablation and defibrillation. A review of studies involving these models and their major findings are provided.
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.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it