Catheter and surgical ablation strategies in atrial fibrillation: what have we learned?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Atrial fibrillation can be eliminated by catheter or surgical ablation, using significantly different approaches and end points. The former has mostly been guided by electrophysiologic recordings, whereas the latter uses direct anatomic visualization. RECENT FINDINGS: Rather than focusing only on ablating triggers of atrial fibrillation, such as pulmonary vein potentials, catheter ablation has evolved toward modification of the left atrial tissue substrate. Correlation of ablation sites with vagal denervation appears to enhance the success of ablation. Integration and eventually registration of spiral CT or MRI images with direct electrophysiologic intracardiac signals will lead to superimposition of true anatomic-electrophysiologic sites, providing enhanced accuracy during mapping. As for surgery, combining endocardial surgical ablation at the time of valvular heart surgery in patients who also have atrial fibrillation has become an integral aspect of surgery. The development of minimally invasive surgery has led to epicardial ablation of atrial fibrillation and even removal of the left atrial appendage, without even entering the heart. SUMMARY: The favorable benefit-to-risk profile, associated with improved outcomes, should eventually lead to reduced morbidity and mortality associated with atrial fibrillation.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".