Electrophysiological Changes Preceding the Onset of Atrial Fibrillation after Coronary Bypass Grafting Surgery
Bibliographic record
Abstract
BACKGROUND: The incidence of Post-CABG atrial fibrillation (AF) lies between 25% and 40%. It worsens morbidity and raises post-operative costs. Detection of incoming AF soon enough for prophylactic intervention would be helpful. The study is to investigate the electrophysiological changes preceding the onset of AF and their relationship to the preoperative risk. METHODS AND RESULTS: Patients were recorded continuously for the first four days after coronary artery bypass grafting surgery (CABG) with three unipolar electrodes sutured to the atria (AEG). The patients experiencing an AF lasting more than 10 minutes were selected and the two hours before the onset were analyzed. Four variables were found to show significant changes in the two hours prior to the first prolonged AF: increasing rate of premature atrial activation, increasing incidence of short transient arrhythmias, acceleration of heart rate, and rise of low frequency content of heart rate. The main contrast was between the first and last hour before AF onset. Preoperative risk was not predictive of the onset time of AF and did not correlate with the amplitude of changes prior to AF. CONCLUSIONS: Post-CABG AF were preceded by electrophysiological changes occurring in the last hour before the onset of the arrhythmia, whereas none of these changes was found to occur in all AF patients. The risk was a weighted sum of factors related to the density of premature activations and the state of atrial substrate reflected by the sinus rhythm and its frequency content prior to AF. Preoperative risk score seems unhelpful in setting a detection threshold for the AF onset.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".