A Rewarming Maneuver Demonstrates the Contribution of Blood Flow to Electrode Cooling During Internally Irrigated RF Ablation
Bibliographic record
Abstract
INTRODUCTION: Titration of radiofrequency (RF) ablation energy to avoid steam pops while maximizing lesion size is difficult because of disparities between electrode and tissue temperature. We hypothesized that the variable contribution of local blood flow to electrode cooling contributes to electrode-tissue temperature disparity, even with an irrigated RF catheter. METHODS AND RESULTS: Forty RF lesions were made in the atria of live swine with an internally irrigated cooled RF catheter. Prior to RF delivery, the catheter tip was cooled and then allowed to rewarm to body temperature by stopping irrigant flow. The rewarming time (RT) was noted. The catheter was again cooled and RF energy delivered, with power titrated to achieve electrode temperature of 40 degrees C, which was previously defined as the approximate upper limit of safety. Lesion formation and steam pops were observed with intracardiac echocardiography. The RT ranged from 10 to 47 seconds. Nine steam pops were seen. The mean RT for lesions with pops was faster than those without (20 vs. 33 seconds, P = 0.003). The 20 lesions with faster RT required more power to achieve target temperature than the 20 lesions with slower RT (P = 0.01). Eight of nine steam pops occurred in the group with faster RT. CONCLUSIONS: RT quantifies convective heating by blood prior to RF. Faster RT predicts greater cooling by blood during irrigated RF, a greater power requirement to reach target electrode temperature, and a higher risk of steam pops during fixed-temperature ablation. RT therefore can demonstrate the variation in blood cooling with each lesion and can be used to tailor energy delivery to maximize efficacy and safety.
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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".