Validation of Dual‐Chamber Pacemaker Diagnostic Data Using Dual‐Channel Stored Electrograms
Bibliographic record
Abstract
BACKGROUND: Pacemaker diagnostic counters are used to guide device programming and patient management. However, these data are susceptible to inappropriate classification of events. The aim of this multicenter study was to evaluate pacemaker diagnostic data using stored intracardiac electrograms (EGMs). METHODS: The study included 351 patients (191 males, aged 71 +/- 10 years) with standard indications for dual-chamber pacemaker implantation. EGM triggers were atrial tachycardia (AT), ventricular tachycardia (VT), sudden bradycardia response (SBR), and pacemaker-mediated tachycardia (PMT). For this study, the devices could store up to 5 EGMs of 8s each (with marker annotation and onset recording). After 3 months, the EGMs were analyzed and classified as "confirmed" if the EGM validated the trigger and as "false positive" if the EGM showed an event different from the trigger. RESULTS: Of the 1,003 EGMs available, the triggers were AT in 640 EGMs, VT in 76, SBR in 105, and PMT in 178 EGMs. Four EGMs were triggered by magnet application. The trigger was confirmed in 614 EGMs (62%): 62% of AT episodes, 18% of VT episodes, 100% of SBR episodes, and 54% of PMT episodes. In 385 cases (45%), the EGMs revealed false-positive events due to far-field sensing (39%), noise and myopotential sensing (26%), sinus tachycardias (21%), double counting (9%), exit block (4%), and undersensing (1%). CONCLUSION: This large-scale study of stored EGMs revealed their value in validating diagnostic counter data. Therapeutic decisions should not be based on diagnostic counters alone; they should be validated by sophisticated tools like stored EGMs.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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".