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Record W4412726258 · doi:10.1093/europace/euaf154

Left bundle branch pacing in patients with structural heart disease: personalizing cardiac resynchronization therapy

2025· review· en· W4412726258 on OpenAlexaff
Jacqueline Joza, Justin Luermans, Vartan Mardigyan, Haran Burri, Marek Jastrzębski, Pugazhendhi Vijayaraman, Kevin Vernooy

Bibliographic record

VenueEP Europace · 2025
Typereview
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsJewish General HospitalMcGill University Health Centre
Fundersnot available
KeywordsCardiac resynchronization therapyCardiologyInternal medicineMedicineHeart failureBundleEjection fractionMaterials science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.900
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.313
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations9
Published2025
Admission routes1
Has abstractyes

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