Left Atrial Appendage characteristics in patients with Persistent Atrial Fibrillation undergoing catheter ablation (LAAPAF Study)
Bibliographic record
Abstract
BACKGROUND: Despite technological and scientific efforts, the recurrence rate of persistent atrial fibrillation (AF) remains high. Several studies have shown that in addition to pulmonary vein (PV) isolation other non-PV triggers, particularly left atrial appendage may be the source of initiation and maintenance of AF. There are few studies showing the role of left atrial appendage (LAA) isolation in order to obtain higher success rate in persistent AF patients. OBJECTIVE: We analyzed the LAA volume, volume index and shape relative to the LA in patients with persistent AF undergoing AF ablation. METHODS: Fifty-nine consecutive patients with persistent AF who underwent catheter ablation were enrolled. Computerized tomography (CT) was performed in order to assess left atrial and PV anatomy including the LAA. Digital subtraction software (GE Advantage Workstation 4.3) was used to separate the LAA from the LA and calculate: LA volume (LAV), LA volume index (LAV/body surface area), LAA volume (LAAV), LAA volume index (LAA volume/LA volume), and LAA morphology [chicken wing (CW) or non-chicken wing (NCW)]. RESULTS: , p=0.71; LAA shape of NCW 50 % vs 52 %, p=0.75, respectively). LAV was significantly correlated with the LAAV (r: o.47, p=0.009). The incidence of NCW LAA was significantly higher in patients with previous stroke/TIA (80 % vs. 20 %, p=0.04). CONCLUSION: The LAA anatomical characteristics (volume/volume index and the shape) were comparable in patients with/out AF recurrence post PVI. It remains to be determined if additional LAA isolation will impact outcomes in patients with persistent AF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".