Preventive Strategies for Atrial Fibrillation After Cardiac Surgery in Nordic Countries
Bibliographic record
Abstract
BACKGROUND AND AIMS: Atrial fibrillation is a common arrhythmia after cardiac surgery. It increases morbidity, length of hospital stay, and costs of operative treatment. Beta-blockers, sotalol, amiodarone, corticosteroids, and biatrial pacing have been shown to be efficient in the prevention of postoperative atrial fibrillation. The aim of this study was to find out how widely different prophylactic strategies for postoperative atrial fibrillation are used in Scandinavian countries. MATERIAL AND METHODS: An online link for a questionnaire was emailed to (214) cardiac surgeons in Finland, Sweden, Norway, Denmark, and Estonia to assess the use of prophylactic methods for postoperative atrial fibrillation. RESULTS: A total of 97 surgeons responded to the survey. Oral beta-blockers were routinely used for atrial fibrillation prophylaxis by 62% of responders. The main reasons for nonuse of beta-blockers were that responders were unconvinced of the evidence of benefit or they preferred some alternative prophylaxis. Intravenous beta-blockers were used frequently by 6% of responders. Amiodarone was used for prophylaxis by 18% of responders. Nonusers were unconvinced of its efficacy, were afraid of its complications, or found its use too cumbersome. Other prophylactic atrial fibrillation strategies that were used are as follows: sotalol by 2%, magnesium by 17%, corticosteroids by 1%, and atrial pacing by 11% of respondents. CONCLUSIONS: There is still widely varying implementation of strategies for atrial fibrillation prophylaxis among Scandinavian cardiac surgeons. Lack of confidence in the efficacy of these approaches is the main rationale for nonimplementation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".