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
Bibliographic record
Abstract
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.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".