<b>High Rate Atrial Tachyarrhythmia Detections in Implantable Pulse Generators</b>: Low Incidence of False‐Positive Detections
Bibliographic record
Abstract
Some newer pulse generators have enhanced diagnostic features that provide information on the frequency, date, time of onset, and duration of atrial and/or ventricular tachyarrhythmias. However, the sensitivity and specificity of device-based atrial tachyarrhythmia detections may vary and depend, in part, on lead position and selected programming parameters. The prevalence of inappropriate detections of paroxysmal atrial fibrillation (PAF) was investigated in 97 patients who received a Thera DR pacemaker 3 months prior to a planned AV node ablation. Patients were randomized to no atrial or to rate adaptive atrial pacing therapy and followed for 3 months. Following a total AV node ablation, patients were randomized to DDDR versus VDD pacing and followed for 1 year. The high rate atrial episode diagnostic feature was used for detection of PAF and the diagnostic data were retrieved during follow-up visits. Criteria were developed to identify oversensing due to near-field P wave detections, far-field R wave detections, or competitive atrial pacing as causes of false-positive atrial tachyarrhythmia detections. A total of 1,636 detections of PAF were recorded in patients preablation. Only 48 episodes (2.9%) were characterized as false-positive detections; 25 episodes (1.5%) were classified as oversensing, and 23 episodes (1.4%) were classified as competitive atrial pacing. A total of 3,061 detections of PAF were recorded postablation. Only four episodes (0.1%) were classified as oversensing. Thus, the diagnostic atrial tachyarrhythmia detection feature in newer pacemakers is an effective method for evaluating the time course of PAF in patients with implantable pulse generators.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".