Heterogeneity in clinical judgment of septal lead position and capture type in left bundle branch area pacing
Bibliographic record
Abstract
BACKGROUND: Determining capture type and septal lead location during left bundle branch area pacing (LBBAP) relies on criteria obtained during implantation. However, during follow-up, the interpretation of left bundle branch (LBB) capture largely depends on QRS morphology, which is not so straightforward in LBBAP. OBJECTIVE: This study aimed to investigate the inter- and intraobserver agreement, as well as the accuracy of clinical judgment of the electrocardiogram (ECG) in determining LBB-capture and septal lead position in patients undergoing LBBAP implantation. In addition, the role of vectorcardiographic QRS-area in determining LBB-capture was evaluated. METHODS: Unipolar paced ECGs during LBBAP implantation from 50 patients with baseline narrow QRS were collected. LBB-capture was attempted in all patients and assessed using MELOS (Multicentre European Left Bundle Branch Area Pacing Outcomes Study) criteria and the European Heart Rhythm Association (EHRA) consensus statement. Eight blinded cardiologists classified 100 ECGs for capture type and septal location. RESULTS: The interobserver and intraobserver agreement for capture type had a Light's kappa of 0.43 and 0.62, respectively. Concordance between clinical judgment and intraprocedural confirmation averaged 72%. Interobserver and intraobserver agreement for septal lead position had a Light's kappa of 0.43 and 0.77 respectively. QRS-area was significantly higher for left ventricular septal pacing (LVSP) than nsLBBP, whereas QRS duration was not. A QRS-area cutoff of 26 mV.ms had 77% accuracy in distinguishing LVSP from nsLBBP. Clinical judgment accuracy averaged 72%. CONCLUSION: Interobserver agreement and correlation with intraprocedural confirmation (gold standard) are only moderate, whereas intraobserver agreement on ECG-based differentiation of capture type and septal lead location is substantial. Vectorcardiographic QRS-area slightly outperforms clinical judgment in distinguishing capture types and may be a useful objective alternative.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".