Esophageal Injury and Temperature Monitoring During Atrial Fibrillation Ablation
Bibliographic record
Abstract
Background— It is common practice to empirically limit the radiofrequency (RF) power when ablating the posterior left atrium during atrial fibrillation ablation to avoid thermal injury to the esophagus. The objective of this study was to determine whether RF energy delivery limited by luminal esophageal temperature (LET) monitoring is associated with a reduction in esophageal injury compared with a strategy of RF power limitation alone. Methods and Results— Eighty-one consecutive patients who underwent atrial fibrillation ablation followed by esophageal endoscopy were included in this observational study. All patients underwent extraostial electric pulmonary vein isolation by using an electroanatomic mapping system and irrigated RF ablation. All RF applications on the posterior left atrium were limited to 35 W. A commercially available, single-thermocouple esophageal probe was used to monitor LET in a subset of patients (n=67). In these cases, applications were promptly interrupted when LET was ≥38.5�C; further applications were performed at reduced power to obtain a LET <38.5�C. Esophageal endoscopy was performed 1 to 3 days after the procedure. Ablation-related esophageal ulcerations were identified in 9 of 81 (11%) patients. All patients were asymptomatic. Of these 81 patients, LET monitoring during ablation occurred in 67 (83%) of patients. Esophageal injury was observed more frequently (36% versus 6%, P <0.006) in the group without LET monitoring. Conclusions— These data suggest that LET monitoring may be associated with a reduction in esophageal injury compared with power limitation alone.
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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 | 0.005 |
| 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 | 0.001 |
| 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".