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Record W4417261053 · doi:10.1093/europace/euaf252

Cardiac implantable electronic device upgrades and downgrades: a <i>Clinical Consensus Statement</i> of the European Heart Rhythm Association (EHRA) of the ESC, the Asia Pacific Heart Rhythm Association (APHRS), Canadian Heart Rhythm Society (CHRS), Heart Rhythm Society (HRS), and the Latin American Heart Rhythm Society (LAHRS)

2025· article· en· W4417261053 on OpenAlexaffabout
Daniel Keene, Jan M. Nielsen, Haran Burri, Carlos A. Chávez-Gutiérrez, Jean‐Claude Deharo, Inga Drossart, James E. Ip, Carsten W. Israel, Jens Brock Johansen, Annamária Kosztin, Chu‐Pak Lau, Shuli Levy, Jaimie Manlucu, Lina Marcantoni, Margarida Pujol‐López, Archana Rao, Christoph Starck, José Marı́a Tolosana, Lieselot van Erven, Julia Vogler, Nandita Kaza

Bibliographic record

VenueEP Europace · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsHeart RhythmDowngradeRhythmCardiac resynchronization therapyAssociation (psychology)GuidelineAtrial fibrillation

Abstract

fetched live from OpenAlex

Cardiac implantable electronic device upgrade and downgrade procedures are increasingly being performed. Whilst the most appropriate guideline-recommended device may have been followed during a patient's initial procedure, the requirements of patients can change over time. This could be due to worsening of cardiac function due to detrimental effects of pacing itself or the diagnosis, development, or progression of another cardiac comorbidity. Device downgrades are also performed when a patient's clinical state changes and are often considered in patients with increased frailty and comorbidity. This clinical consensus statement aims to provide a framework for screening patients for device upgrade, pre-procedural planning considerations, available procedural strategies, namely a summary of techniques and approaches for vascular access, including ipsilateral and contralateral options, and a framework for when extraction to gain access may be appropriate. The document also provides advice on how to frame an ethical discussion with patients and carers on available options.

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)0.036
metaresearch head score (Gemma)0.050
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: Other · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0360.050
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0040.004
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0030.003

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 head0.014
GPT teacher head0.283
Teacher spread0.269 · 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
GenreOther

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

Citations3
Published2025
Admission routes2
Has abstractyes

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