Relationship between time‐to‐isolation and freeze duration: Computational modeling of dosing for Arctic Front Advance and Arctic Front Advance Pro cryoballoons
Bibliographic record
Abstract
Abstract Background Preclinical and clinical studies have utilized periprocedural parameters to optimize cryoballoon ablation dosing, including acute time‐to‐isolation (TTI) of the pulmonary vein, balloon rate of freezing, balloon nadir temperature, and balloon‐thawing time. This study sought to predict the Arctic Front Advance (AFA) vs Arctic Front Advance Pro (AFA Pro) ablation durations required for transmural pulmonary vein isolation at varied tissue depths. Methods A cardiac‐specific, three‐dimensional computational model that incorporates structural characteristics, temperature‐dependent cellular responses, and thermal‐conductive properties was designed to predict the propagation of cold isotherms through tissue. The model assumed complete cryoballoon‐to‐pulmonary vein (PV) circumferential contact. Using known temperature thresholds of cardiac cellular electrical dormancy (at 23°C) and cellular nonviability (at −20°C), transmural time‐to‐isolation electrical dormancy (TTIED) and cellular nonviability (TTINV) were simulated. Results For cardiac thickness of 0.5, 1.25, 2.0, 3.0, 4.0, and 5.0 mm, the 23°C isotherm passed transmurally in 33, 38, 46, 62, 80, and 95 seconds during cryoablation utilizing AFA and 33, 38, 46, 63, 80, and 95 seconds with AFA Pro. Using the same cardiac thicknesses, the −20°C isotherm passed transmurally in 40, 55, 78, 161, 354, and 696 seconds during cryoablation with AFA and 40, 54, 78, 160, 352, and 722 seconds with AFA Pro. Conclusion This model predicted a minimum duration of cryoballoon ablation (TTINV) to obtain a transmural lesion when acute TTI of the PV was observed (TTIED). Consequently, the model is a useful tool for characterizing CBA dosing, which may guide future cryoablation dosing strategies.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".