Contact-Force Guided Pulmonary Vein Isolation does not Improve Success Rate in Persistent Atrial Fibrillation Patients and Severe Left Atrial Enlargement: A 12-month Follow-Up Study
Bibliographic record
Abstract
BACKGROUND: Catheter ablation is a cornerstone treatment strategy in atrial fibrillation (AF). Left atrial (LA) size is one of the contributors in development of AF recurrences. The impact of contact-forced (CF) guided catheter ablation on the success rate of persistent AF patients with severe enlarged LA has not been investigated yet. METHODS: Sixty-six patients with diagnosis of longstanding persistent AF undergoing catheter ablation were enrolled. All patients underwent a standard transthoracic echocardiography according to the guidelines. LA size was considered severely enlarged when LA diameter was ≥ 50 mm. CF catheter ablation with a Tacticath Quartz catheter (St Jude Medical, St. Paul, MN, USA) was used in all patients. RESULTS: The mean age was 61.9 ± 9.9 years, and LAD 47.8 ± 11.6 mm. Among 66 patients with persistent AF, 32 (48%) patients were diagnosed with AF recurrences. Twenty-eight (42%) patients had severely enlarged LA. The recurrence of AF was comparable in patients with and without severe enlarged LA (47% vs. 42%, p=0.79). The recurrence of AF was lower in patients who underwent CF-guided ablation with a normal LA dimension (36 %, p=0.54). Procedure duration was longer in patients with severely enlarged LA. LA dimension was not significantly different between patients with and without AF recurrence (49.8 ± 7.9 mm vs. 45.9 ± 7.5 mm, p=0.15). LAD and was significantly correlated with the time to recurrence of AF (r:-0.60, p=0.02). CONCLUSION: Our preliminary findings have demonstrated that CF guided ablation does not improve the success rate in longstanding persistent AF patients with severe LA enlargement.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".