An update and current expert opinions on percutaneous left atrial appendage occlusion for stroke prevention in atrial fibrillation
Bibliographic record
Abstract
Oral anticoagulation (OAC) remains the mainstream therapy for ischaemic stroke prevention in patients with atrial fibrillation (AF). However, for patients contraindicated to OAC and those who experienced a stroke while on therapeutic OAC, no reasonable pharmacotherapy is available. Although surgical left atrial appendage (LAA) excision offers a non-pharmacological alternative, effective stroke prevention by this treatment is not demonstrated by randomized clinical studies. Percutaneous occlusion of the LAA may be an alternative therapy for selected AF patients. Recently reported results confirm the technical feasibility of this technique and its effectiveness in preventing ischaemic stroke. With increasing operator experience, successful and event-free device implantation is achieved in typically 97% of the cases. Moreover, in non-randomized cohorts implanted with LAA occlusion devices, stroke rates are markedly reduced compared with rates predicted by risk stratification schemes such as CHADS2 and CHA2DS2-VASc. This paper summarizes recently published results from clinical studies on percutaneous LAA occlusion and current expert opinions with respect to patients who may be suitable for this therapy. In addition, several aspects regarding the safety of device implantation for LAA occlusion and follow-up of patients are discussed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".