Evaluation of Index of Cardio-Electrophysiological Balance in Patients With Atrial Fibrillation on Antiarrhythmic-Drug Therapy
Bibliographic record
Abstract
BACKGROUND: Index of cardio-electrophysiological balance (iCEB) has been described as a novel risk marker for predicting malignant ventricular arrhythmia. There remains limited evidence on the effects of amiodarone and propafenone used for sinus rhythm maintenance on iCEB in patients with atrial fibrillation (AF). The aim of this study was to evaluate iCEB in patients with AF on antiarrhythmic-drug therapy. METHODS: A total of 108 patients with AF (68 patients using amiodarone and 40 patients using propafenone) and 50 healthy subjects were included in the study. All groups underwent a standard 12-lead surface electrocardiogram. QRS duration, QT, T wave peak-to-end (Tp-e) intervals, iCEB (QT/QRS) and iCEBc (heart rate-corrected QT (QTc)/QRS) rates were calculated from the electrocardiogram and compared between groups. RESULTS: QT, Tp-e intervals and Tp-e/QT ratio were significantly longer in the amiodarone group than the propafenone and control groups (P < 0.001, for all). iCEB was similar in the amiodarone and control groups (4.4 ± 0.6 and 4.2 ± 0.4; P > 0.05), while iCEB values in the propafenone group were significantly lower than the amiodarone group and control groups (3.9 ± 0.5; P < 0.001). There was a significantly difference in iCEBc values among the amiodarone, control and propafenone groups (4.8 ± 0.6, 4.6 ± 0.4 and 4.3 ± 0.6; P < 0.001, respectively). CONCLUSIONS: In this study, higher iCEBc parameters were observed in patients using amiodarone, while iCEBc values were lowest among patients with AF using propafenone. Further studies are needed to determine whether these electrophysiological changes are associated with ventricular arrhythmias for patients with AF on antiarrhythmic-drug therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".