Atrial fibrillation: Current and emerging surgical strategies
Bibliographic record
Abstract
OBJECTIVE: (a) To provide a comprehensive review of current literature on the surgical management of atrial fibrillation (AF), highlighting surgical approaches and outcomes. (b) To summarize the latest guidelines pertinent to the surgical management of AF. BACKGROUND: AF is associated with high rates of morbidity and mortality, primarily related to the associated risk of stroke. The mainstay of management is pharmacologic rate or rhythm control and catheter-based ablation. Surgical ablation (SA) is an alternative strategy that is effective in select patient populations. Recently, novel techniques and technologies have been introduced and this has expanded the surgical capacity to manage AF. METHODS: A comprehensive review of the literature was conducted. RESULTS: Surgery can be a highly effective alternative therapeutic option for the management of AF in the appropriate patient population. The need for permanent pacemaker implantation is controversial among patients undergoing surgical intervention for AF. Surgical outcomes are promising, with long-term control of AF and symptomatic relief achieved in select groups of patients. CONCLUSIONS: This article provides a comprehensive review of the surgical management of AF. We have summarized the latest surgical outcomes and contextualized the most recent guidelines.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".