Correlation of unipolar electrogram modification with ablation index during pulmonary vein isolation: A pilot study
Bibliographic record
Abstract
BACKGROUND: Pulmonary vein isolation (PVI) using radiofrequency catheter ablation is a widely accepted therapy for drug-refractory atrial fibrillation patients. Elimination of the negative component of the local unipolar electrogram (UEGM) during PVI is a marker of transmural lesion formation. The ablation index (AI) can predict the quality of ablation lesion. Combining these two parameters could make PVI safer and efficient. The purpose of this pilot study was to examine the correlation between UEGM modification characteristics of the different target areas of left atrium and the associated AI values during PVI. METHODS: We analyzed 10 patients who underwent PVI using radiofrequency energy. The local electrophysiological properties and ablation parameters of 15 designated areas of interest in the left atria targeted by radiofrequency catheter ablation were collected. RESULTS: Out of the 10 patients, six were men (mean age 66 years) and 80% had paroxysmal AF. The mean time to achieve the UEGM modification in the posterior wall was shorter than that of the anterior wall (8.9 seconds vs. 11.1 s, respectively). The UEGM modification for every lesion was achieved at significantly lower AI values than conventional AIs (p < .001). CONCLUSION: During PVI, the AIs deduced according to the local UEGM modification are markedly shorter than those generally recommended AIs in contemporary practice. This indicates that conventionally recommended AIs could be safely reduced while ensuring the efficacy and quality of radiofrequency ablation during PVI. This approach would probably reduce to risk of collateral thermal injuries.
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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 | 0.004 |
| 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".