Optimal interlesion distance for 90 and 50 watt radiofrequency applications with low ablation index values: experimental findings in a chronic ovine model
Bibliographic record
Abstract
AIMS: The optimal interlesion distance (ILD) for 90 and 50 W radiofrequency applications with low ablation index (AI) values in the atria has not been established. Excessive ILDs can predispose to interlesion gaps, whereas restrictive ILDs can predispose to procedural complications. The present study sought, therefore, to experimentally determine the optimal ILD for 90 W-4 s and 50 W applications with low AI values to optimize catheter ablation outcomes in humans. METHODS AND RESULTS: Posterior intercaval lines were created in eight adult sheep using CARTO and the QDOT-MICRO catheter in a temperature-controlled mode. In four animals, the lines were created with 50 W applications, a target AI value ≥350, and ILDs of 6, 5, 4, and 3 mm, respectively. In the other four animals, the lines were created with 90 W-4 s applications and ILDs of 6, 5, 4, and 3 mm, respectively. Activation maps were created immediately after ablation and at 21 days to assess linear block prior to gross and histological analyses. All eight lines appeared transmural and continuous on histology. However, for 50 W-only applications with an ILD of 3 mm resulted in durable linear electrical block, whereas for 90 W applications, only the lines with ILDs of 4 and 3 mm were blocked. No complications were detected during ablation procedures, but all power and ILD combinations except 50 W-6 mm resulted in asymptomatic shallow lung lesions. CONCLUSION: In the intercaval region in sheep, for 50 W applications with an AI value of ∼370, the optimal ILD is 3 mm, whereas for 90 W-4 s applications, the optimal ILD is 3-4 mm.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".