Cardiac Pulsed Field Ablation Lesion Durability Assessed by Polarization-Sensitive Optical Coherence Reflectometry
Bibliographic record
Abstract
BACKGROUND: Pulsed field ablation uses electrical fields to cause nonthermal cell death over several hours. Polarization-sensitive optical coherence reflectometry is an optical imaging technique that can detect changes in the tissue ultrastructure in real time, which occurs when muscular tissue is damaged. The objective of this study was to evaluate the ability of a polarization-sensitive optical coherence reflectometry system to predict the development of chronic lesions based on acute changes in tissue birefringence during pulsed field ablation. METHODS: Superior vena cava isolation was performed in 30 swine using a biphasic, bipolar pulsed field ablation system delivered with a nonirrigated focal tip catheter. Acute changes in tissue birefringence and voltage abatement were analyzed for each individual lesion. A high-resolution electroanatomical map was performed at baseline and 4 to 12 weeks after ablation to locate electrical gaps in the ablated area. RESULTS: A total of 141 lesions were delivered and included in the analysis. Acute electrical isolation based on the electroanatomical map was achieved in 96% of the animals, but chronic isolation was only seen in 14 animals (46%). The mean voltage abatement of lesions that showed recovery was 82.8%±14.6% versus 84.4%±17.4% for those that showed fibrosis ( P =0.7). The mean acute reduction in tissue birefringence in points demonstrating fibrosis was 63.8%±11.3% versus 9.1%±0.1% in the points that resulted in electrical gaps. A threshold of acute reduction of birefringence of ≥20% could predict chronic lesion formation with a sensitivity of 96% and a specificity of 83%. CONCLUSIONS: Acute tissue birefringence changes assessed with polarization-sensitive optical coherence reflectometry during pulsed field ablation can predict chronic lesion formation and guide the ablation procedure although limited by the tissue thickness.
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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 | 0.002 |
| 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".