Determination of single cryoablation outcome within 30 to 60 seconds of freezing based on ice impedance
Bibliographic record
Abstract
BACKGROUND: A direct indicator of effective pulmonary vein isolation (PVI) based on early ice formation is presently lacking. OBJECTIVE: The initial impedance rise within 30 to 60 seconds (sec) of single cryoablation relating to ice on the distal surface of the cryoballoon could; predict effective PVI with early termination, the need for prolonging the cryoablation, or failure to achieve effective ablation. METHODS: Impedance measurements were taken between two ring electrodes, at the anterior balloon surface and at the shaft behind the balloon. Ice covering the anterior ring leads to impedance rise. Single cryoablation (eight animals, 37 veins) was applied for 90 to 180 sec. Cryoapplication was terminated if the impedance reached ≥500 Ω. Impedance levels at ≤60 sec of cryoablation were divided into three groups based on the characteristics of the impedance rise. PVI was confirmed acutely and at 45 ± 9 days recovery by electrophysiology mapping and histopathology. RESULTS: At 60 sec of freezing, an impedance rise of 34.1 ± 15.2 Ω (13-50 Ω) and slope of the impedance rise (measured during 15-30 sec of cryoapplication) less than 1 Ω/sec resulted in failed PVI. An impedance rise of 104.4 ± 31.5 Ω (76-159 Ω) and slope of 2 Ω/sec resulted in 100% PVIs. An impedance rise of 130.9 ± 137.8 Ω (40-590 Ω) and slope of 10 Ω/sec resulted in 100% PVIs with early termination at 90 sec. CONCLUSION: The efficacy of single cryoablation can be defined within 30 to 60 sec based on ice impedance. Three unique impedance profiles described in this investigation are associated with the uniformity and thickness of the ice buildup on the anterior surface of the balloon. One cryoablation with an adequate impedance rise is needed for successful outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".