Left bundle branch pacing in patients with structural heart disease: personalizing cardiac resynchronization therapy
Bibliographic record
Abstract
Biventricular pacing remains the cornerstone of cardiac resynchronization therapy (CRT) in patients with heart failure, with well-established benefits. Left bundle branch pacing (LBBP) offers a physiologic alternative by engaging the native conduction system to restore synchrony and has generated significant enthusiasm. However, the growing adoption of LBBP should be tempered by recognition that a one-size-fits-all approach may not address the underlying substrate, particularly in those with intraventricular conduction delay. While a less-than-optimal LBBP implant may be sufficient in bradycardia patients, its adequacy in heart failure patients, who may require more precise consideration of conduction disease, remains uncertain. This review gives a comprehensive framework for integrating LBBP into CRT, including pre-implant, intraprocedural, and post-implant assessment. It also provides practical guidance on when to pursue LBBP alone, when to supplement with a coronary sinus lead, and when to consider conventional biventricular pacing, with an emphasis on a personalized approach to the underlying conduction substrate for maximal therapeutic benefit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".