Brugada Syndrome: Evolving Insights and Emerging Treatment Strategies
Bibliographic record
Abstract
Brugada syndrome (BrS) is a rare inherited arrhythmia disorder associated with sudden cardiac death secondary to malignant ventricular arrhythmias. Since its first mention approximately 25 years ago, major strides have been made towards unraveling the condition's genetic and mechanistic underpinnings. Despite considerable progress, however, gaps in the understanding of BrS continue to persist, and clinical management of affected individuals remains challenging. Identification of an underlying genetic culprit continues to be elusive in the majority of patients, while discord regarding the condition's underlying pathophysiology also persists, with strong lines of evidence present for both the "depolarization" and "repolarization" hypotheses. Exciting new therapeutic options hold significant promise, including substrate-based catheter ablation and the subcutaneous implantable cardioverter-defibrillator, although the decision of when to intervene in the cases of asymptomatic patients remains unclear. Provided that the risk of events in BrS is not truly stochastic, distinct sub-phenotypes of the condition, possessing variable levels of arrhythmic risk, may exist, and their identification may lead to the improved care of BrS patients and their families.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".