A Systematic Review of Ongoing Registered Research Studies on Post-Operative Atrial Fibrillation after Cardiac Surgery
Bibliographic record
Abstract
Background/Objectives: New-onset atrial fibrillation (AF) after cardiac surgery is associated with patient-important outcomes. Uncertainty persists regarding its prevention, detection, and management. This review seeks to identify, compile, and describe ongoing registered research studies involving patients with or at risk for post-operative AF (POAF) after cardiac surgery. Methods: We searched clinical trial registries in January 2023 for studies focusing on POAF prediction, prevention, detection, or management. We extracted data from each record and performed descriptive analyses. Results: In total, 121 studies met the eligibility criteria, including 82 randomized trials. Prevention studies are the most common (n = 77, 63.6%), followed by prediction (n = 21, 17.4%), management (n = 16, 13.2%), and detection studies (n = 7, 5.8%). POAF after cardiac surgery is an area of active research. Conclusions: There are many ongoing randomized prevention studies. However, two major clinical gaps persist; future randomized trials should compare rate and rhythm control in patients who develop POAF, and long-term follow-up studies should investigate strategies to monitor for AF recurrence in patients with POAF.
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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 | 0.016 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".