Automatic analysis of pacemaker diagnostic data in the identification of atrial tachyarrhythmias in patients with no prior history of them
Bibliographic record
Abstract
AIMS: Modern pacemakers provide a large amount of diagnostic data. Given the limited time available during a pacemaker follow-up visit essential information may be overlooked. This registry was conducted to assess the utility of an expert system that analyses the diagnostic data collected by an implanted pacing device and notifies and advises the physician about suspected technical issues and arrhythmias that need further attention. METHODS: Patients with various standard indications for pacing were included in this registry and received single or dual chamber pacemakers. Data were collected and analysed by the expert system during at least two subsequent follow-up visits. The evaluation of this system focused on data obtained from patients with a dual chamber pacing device without prior history of atrial arrhythmias. RESULTS: A total number of 239 patients without prior history of atrial tachyarrhythmias were included in this analysis. Atrial tachyarrhythmias were detected in 73 (31%) of these patients. The highest incidence of newly detected arrhythmias occurred in the group of patients with high-degree AV block and VDD pacemakers. Furthermore, newly detected arrhythmias predominantly occurred in the period shortly after implantation. Device programming suggestions by the expert system were adopted in 30% of the cases. Following detection of atrial tachyarrhythmias by the expert system, pharmacological management was adjusted at 71% of the first follow-up visits and at 27% of later follow-up visits. CONCLUSION: Results of this registry show that this expert system provides a valuable tool for the detection of atrial tachyarrhythmias during pacemaker follow-up visits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".