Postoperative atrial fibrillation after cardiac surgery: what is new?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Since postoperative atrial fibrillation (POAF) after cardiac surgery remains a common clinical problem and is associated with adverse clinical outcomes, considerable research efforts are spent to better understand and inform its management. This review highlights recent studies on this topic. RECENT FINDINGS: A PubMed review of published research on POAF after cardiac surgery over the past two years was conducted. Papers were selected on the basis of their potential value to enhance clinical practice. This search yielded studies which have advanced our understanding on the incidence of late-onset POAF after cardiac surgery and its predictive factors. This information may be useful for clinicians on the optimal timing for atrial fibrillation detection after cardiac surgery. Due to a lack of dedicated randomized trial data, the optimal stroke prevention approach remains uncertain in this patient population. SUMMARY: POAF after cardiac surgery is an active area of research. Recent studies have provided additional insights on the risk of late-onset atrial fibrillation (>3 months) after cardiac surgery. This information may help clinicians identify patients who are more likely to experience recurrent atrial fibrillation after cardiac surgery. Ongoing randomized trials will help clarify the optimal stroke prevention strategy in this patient population.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".