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Record W4413137465 · doi:10.1016/j.ipej.2025.08.002

Usefulness of aVR sign as a predictor of sudden cardiac death or appropriate ICD shocks in Brugada syndrome: A systematic review and meta-analysis of cohort studies

2025· article· en· W4413137465 on OpenAlexaboutno aff
Jonathan Vincent Lee, Hendyono Lim, Nicolaus Novian Dwiya Wahjoepramono

Bibliographic record

VenueIndian Pacing and Electrophysiology Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicCardiac electrophysiology and arrhythmias
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBrugada syndromeSudden cardiac deathInternal medicineCardiologySudden deathOdds ratioCohortPopulationCohort studyImplantable cardioverter-defibrillator

Abstract

fetched live from OpenAlex

INTRODUCTION: Several electrocardiograph markers are proposed as predictors of life-threatening arrhythmia in Brugada Syndrome, including the aVR sign. However, results of previous studies were inconsistent. Therefore, we aim to determine whether the aVR sign can predict sudden cardiac death in BrS patients. METHODS: We extracted data from PubMed, Cochrane, and EBSCO using MeSH keywords "Brugada syndrome, sudden cardiac death, arrhythmia". Inclusion criteria include cohorts from the last 10 years of the BrS population with the aVR sign as a predictor. We excluded patients with channelopathies other than Brugada syndrome and low-quality studies. We assessed the quality of studies using the Newcastle-Ottawa Scale. Data will be presented as odds ratios with 95 % confidence intervals. The endpoint is life-threatening arrhythmia resulting in sudden cardiac death. RESULTS: = 0 %). R/q ratio ≥0.75 in aVR is also associated with increased risk. All studies were considered good quality based on the Newcastle-Ottawa scale. These markers may be integrated with other factors to identify the high-risk patients. CONCLUSION: aVR sign can predict sudden cardiac death in Brugada syndrome and this marker may be considered for risk assessment and lead the management strategy for better prevention.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.298
Teacher spread0.276 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

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

Citations1
Published2025
Admission routes1
Has abstractyes

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