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Record W4365998148 · doi:10.1093/europace/euad044

EHRA clinical consensus statement on conduction system pacing implantation: executive summary. Endorsed by the Asia-Pacific Heart Rhythm Society (APHRS), Canadian Heart Rhythm Society (CHRS) and Latin-American Heart Rhythm Society (LAHRS)

2023· article· en· W4365998148 on OpenAlexaffabout
Haran Burri, Marek Jastrzębski, Óscar Cano, Karol Čurila, Jan De Pooter, Weijian Huang, Carsten W. Israel, Jacqueline Joza, Jorge Romero, Kevin Vernooy, Pugazhendhi Vijayaraman, Zachary I. Whinnett, Francesco Zanon

Bibliographic record

VenueEP Europace · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsHeart RhythmRhythmMedicineCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Conduction system pacing (CSP) has emerged as a more physiological alternative to right ventricular pacing and is also being used in selected cases for cardiac resynchronization therapy. His bundle pacing was first introduced over two decades ago and its use has risen over the last years with the advent of tools which have facilitated implantation. Left bundle branch area pacing is more recent but its adoption is growing fast due to a wider target area and excellent electrical parameters. Nevertheless, as with any intervention, proper technique is a prerequisite for safe and effective delivery of therapy. This document aims to standardize the procedure and to provide a framework for physicians who wish to start CSP implantation, or who wish to improve their technique. A synopsis is provided in this print edition of EP-Europace. The full document may be consulted online, and a 'Key Messages' App can be downloaded from the EHRA website.

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 imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.045
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0040.003
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0250.026

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 head0.041
GPT teacher head0.324
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations123
Published2023
Admission routes2
Has abstractyes

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