Computational Modelling Of Radiofrequency Cardiac Ablation To Study The Effect Of Cooling On Lesion Parameters
Bibliographic record
Abstract
Radiofrequency ablation (RFA) is a technique used to treat cardiac arrhythmias.It creates lesions in the heart by creating thermal damage.Due to limitations associated with in vivo as well as in vitro studies, computational methods assist in further analysis of the problem by allowing for quicker and more diverse parametric studies and hence, a more thorough understanding of the physics involved.These computational models have been proven to be good representations of the process by accurately modelling the catheter with simplified geometry and boundary conditions.Although these studies have inconsistencies in material properties (due to the variation of thermal and mechanical properties in biological tissue) as well as different methods of creating the geometry and applying the boundary conditions, overall they are quite similar.The effects of esophageal cooling were investigated to understand its effect on the process.It was determined that using the standard model found within the literature, the esophageal cooling changed the lesion depth by less than 18%, while changing the maximum tissue temperature by as much as 13.4%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".