Analysis of Implantable Cardioverter Defibrillator Therapy in the Antiarrhythmics Versus Implantable Defibrillators (AVID) Trial
Bibliographic record
Abstract
INTRODUCTION: The implantable cardioverter defibrillator (ICD) is commonly used to treat patients with documented sustained ventricular tachycardia (VT) or ventricular fibrillation (VF). Arrhythmia recurrence rates in these patients are high, but which patients will receive a therapy and the forms of arrhythmia recurrence (VT or VF) are poorly understood. METHODS AND RESULTS: The therapy delivered by the ICD was examined in 449 patients randomized to ICD therapy in the Antiarrhythmics Versus Implantable Defibrillators (AVID) Trial. Events triggering ICD shocks or antitachycardia pacing (ATP) were reviewed for arrhythmia diagnosis, clinical symptoms, activity at the onset of the arrhythmia, and appropriateness and results of therapy. Both shock and ATP therapies were frequent by 2 years, with 68% of patients receiving some therapy or having an arrhythmic death. An appropriate shock was delivered in 53% of patients, and ATP was delivered in 68% of patients who had ATP activated. The first arrhythmia treated in follow-up was diagnosed as VT (63%), VF (13%), supraventricular tachycardia (18%), unknown arrhythmia (3%), or due to ICD malfunction or inappropriate sensing (3%). Acceleration of an arrhythmia by the ICD occurred in 8% of patients who received any therapy. No physical activity consistently preceded arrhythmias, nor did any single clinical factor predict the symptoms of the arrhythmia. CONCLUSION: Delivery of ICD therapy in AVID patients was common, primarily due to VT. Inappropriate ICD therapy occurred frequently. Use of ICD therapy as a surrogate endpoint for death in clinical trials should be avoided.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".