Percutaneous left atrial appendage closure for stroke prevention in atrial fibrillation: who should receive it in current clinical practice?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Although oral anticoagulation (OAC) remains the cornerstone therapy for stroke prevention in atrial fibrillation, several limitations, such as noncompliance and bleeding, limit its effectiveness. Percutaneous left atrial appendage closure (pLAAC) has emerged as a promising therapy. We will review current and potential indications for pLAAC and knowledge gaps. RECENT FINDINGS: Current guidelines recommend pLAAC for patients who have atrial fibrillation at moderate to high risk of stroke with a high risk of bleeding or who have a contraindication for OAC. pLAAC is being investigated as a potential therapeutic option for the following patient populations: end-stage renal disease, after atrial fibrillation ablation, and in combination with OAC in patients with a high risk of breakthrough stroke or in patients with prior stroke on OAC. The Left Atrial Appendage Occlusion Study IV (LAAOS-IV) ( n = 4000) is a randomized trial that will determine the role of pLAAC and OAC compared to OAC alone in preventing ischemic stroke or systemic embolism. SUMMARY: pLAAC has a growing role in patients with atrial fibrillation with moderate to high stroke risk and contraindication to OAC. Multiple randomized trials are currently underway in different patient populations, which may expand the role of pLAAC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".