Comparison of Induced and Spontaneous Atrial Tachyarrhythmias in Patients with a History of Spontaneous Atrial Tachyarrhythmias
Bibliographic record
Abstract
INTRODUCTION: This retrospective study investigated whether induced episodes could be used to predict the morphology of future spontaneous atrial episodes. METHODS: Eighty-two patients (64 +/- 12 years; 77% male; CAD in 60%; left ventricular ejection fraction 45 +/- 16%) with a history of atrial tachycardia or atrial fibrillation (AT/AF) were implanted with a dual-chamber implantable cardioverter defibrillator (ICD) and followed for 6 months. A total of 224 episodes of induced and spontaneous AT/AF were classified into type I, II, and III according to the method of Israel et al. and then compared based on average cycle length (CL) and atrial amplitude. Episodes were also grouped as "pace-terminable" or "nonpace-terminable" based on the CL definition of Gillis et al. RESULTS: The analysis of 121 induced episodes (from 80 patients) and 103 spontaneous episodes (from 43 patients) showed that within each arrhythmia type, there were no significant differences in CL or mean amplitude between induced and spontaneous episodes. Additional analysis of patients that had both induced and spontaneous episodes (n = 41) showed 78% had at least one spontaneous episode that matched the induced episode. Fifty-seven percent of spontaneous episodes were considered to be pace-terminable based on CL. CONCLUSIONS: Our data suggest that there is no significant difference between induced and spontaneous episodes of AT/AF of the same type. The majority of patients had at least one spontaneous episode of the same type as the induced episode, showing that induced atrial arrhythmias may be useful in predicting the morphology of future spontaneous episodes and in identifying patients potentially benefiting from atrial antitachycardia pacing.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".