Outcome after ablation of atypical atrial flutter: Is induction a feasible approach?
Bibliographic record
Abstract
Background: Atypical atrial flutter (AAF) is an increasingly relevant clinical problem. Despite advancements in mapping and ablation techniques, the general management of these patients remain challenging especially when mapping cannot be performed during ongoing arrhythmia. There are no data whether induction of AAF is a feasible approach in these cases. Methods: We retrospectively analyzed patients who underwent catheter ablation of AAF and compared procedural results between patients with ongoing tachycardia when starting the procedure and patients with induced AAF. Results: We analyzed 97 ablation procedures performed in 76 patients with a mean follow-up of 13.2 ± 12.2 months. In 68 procedures (70.1 %) AAF was ongoing at the beginning of the procedure and in 29 cases (29.9 %) AAF had to be induced.There was no statistically significant difference regarding acute procedural success. The recurrence rate of any arrhythmia during follow-up was significantly higher after ablation of ongoing AAF compared to induced AAF (63.2 % vs. 42.9 %; p = 0.047) driven by a significant higher rate of AAF-recurrence (57.4 % vs. 34.5 %; p = 0.039). The number of ablated tachycardias per patient as well as the number of de-novo tachycardias found during re-ablation showed no significant difference between both groups. Conclusion: Starting a procedure with ongoing arrhythmia did not result in better short- or mid-term outcome in patients undergoing AAF ablation. Furthermore, based on our results inducing AAF seems a legitimate approach for AAF ablation in patients presenting in sinus rhythm.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".