Coupled Thermo-Electro-Mechanical Models of Cardiac Ablation at Tissue-Cellular Scales and a Role of Microtubules
Bibliographic record
Abstract
Radiofrequency ablation is a medical procedure that is becoming increasingly used for disease treatments. During this procedure, part of dysfunctional tissues is ablated by using the heat, typically generated from medium frequency electric current. It is a field of medicine where mathematical and computational models play a substantial role in assisting clinical practitioners with quantifications of some of the most critical characteristics, including temperature distributions and ablated volumes. In this contribution, we describe a framework for the development of coupled thermo-electro-mechanical models in this field. While our framework and the described validation procedures can be applicable to a variety of ablation modalities and treatments, a major focus has been given to some of the pecularities related to cardiac ablation at tissue-cellular scales and a role played by cell organelles such as microtubules, as well as by the cell nucleus. We have discussed the effects their inclusion makes on the calculation of the main characteristics of the radiofrequency ablation procedures. The importance of domain heterogeneity, as well as the integration of fluid-structure interaction in the developed framework along with other effects, have been highlighted and the details on ablation modalities in the context of clinical experimental research have been given. Finally, future generalizations of the proposed framework with hybrid stochastic-deterministic models have been put forward.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".