Electrocardiographic characterization of non-selective His-bundle pacing: validation of novel diagnostic criteria
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Bibliographic record
Abstract
AIMS: Permanent His-bundle (HB) pacing is usually accompanied by simultaneous capture of the adjacent right ventricular (RV) myocardium-this is described as a non-selective (ns)-HB pacing. It is of clinical importance to confirm HB capture using standard electrocardiogram (ECG). Our aim was to identify ECG criteria for loss of HB capture during ns-HB pacing. METHODS AND RESULTS: Patients with permanent HB pacing were recruited. Electrocardiograms during ns-HB pacing and loss of HB capture (RV-only capture) were obtained. Electrocardiogram criteria for loss/presence of HB capture were identified. In the validation phase, these criteria and the 'HB ECG algorithm' were tested using a separate, sizable set of ECGs. A total of 353 ECG (226 ns-HB and 128 RV-only) were obtained from 226 patients with permanent HB pacing devices. QRS notch/slur in left ventricular leads and R-wave peak time (RWPT) in lead V6 were identified as the best features for differentiation. The 'HB ECG algorithm' based on these features correctly classified 87.1% of cases with sensitivity and specificity of 93.2% and 83.9%, respectively. The criteria for definitive diagnosis of ns-HB capture (no QRS slur/notch in Leads I, V1, V4-V6, and the V6 RWPT ≤ 100 ms) presented 100% specificity. CONCLUSION: A novel ECG algorithm for the diagnosis of loss of HB capture and criteria for definitive confirmation of HB capture were formulated and validated. The algorithm might be useful during follow-up and the criteria for definitive confirmation of ns-HB capture offer a simple and reliable ancillary procedural endpoint during HB device implantation.
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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 | 0.000 | 0.000 |
| 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 it