Interatrial block and interatrial septal thickness in patients with paroxysmal atrial fibrillation undergoing catheter ablation: Long‐term follow‐up study
Bibliographic record
Abstract
BACKGROUND: Interatrial block (IAB) is a strong predictor of recurrence of atrial fibrillation (AF). IAB is a conduction delay through the Bachman region, which is located in the upper region of the interatrial space. During IAB, the impulse travels from the right atrium to the interatrial septum (IAS) and coronary sinus to finally reach the left atrium in a caudocranial direction. No relation between the presence of IAB and IAS thickness has been established yet. OBJECTIVE: To determine whether a correlation exists between the degree of IAB and the thickness of the IAS and to determine whether IAS thickness predicts AF recurrence. METHODS: Sixty-two patients with diagnosis of paroxysmal AF undergoing catheter ablation were enrolled. IAB was defined as P-wave duration ≥120 ms. IAS thickness was measured by cardiac computed tomography. RESULTS: Among 62 patients with paroxysmal AF, 45 patients (72%) were diagnosed with IAB. Advanced IAB was diagnosed in 24 patients (39%). Forty-seven patients were male. During a mean follow-up period of 49.8 ± 22 months (range 12-60 months), 32 patients (51%) developed AF recurrence. IAS thickness was similar in patients with and without IAB (4.5 ± 2.0 mm vs. 4.0 ± 1.4 mm; p = .45) and did not predict AF. Left atrial size was significantly enlarged in patients with IAB (40.9 ± 5.7 mm vs. 37.2 ± 4.0 mm; p = .03). Advanced IAB predicted AF recurrence after the ablation (OR: 3.34, CI: 1.12-9.93; p = .03). CONCLUSIONS: IAS thickness was not significantly correlated to IAB and did not predict AF recurrence. IAB as previously demonstrated was an independent predictor of AF recurrence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".