Updates on inherited arrhythmia syndromes (Brugada syndrome, long QT syndrome, CPVT, ARVC)
Bibliographic record
Abstract
The inherited arrhythmia (IA) syndromes are a group of rare and complex conditions that may predispose individuals to ventricular arrhythmias and sudden cardiac death. Our understanding of the genetic architecture underlying these syndromes has evolved, with recent reappraisals of variant pathogenicity and quantification of polygenic influences. The IA population includes an increasing proportion of low-risk patients, often identified via familial screening; avoiding over-treatment in these patients is an important consideration. Conversely, high-risk patients have an expanding armamentarium of targeted therapeutic interventions available beyond the ICD, with many emerging novel therapies. Refined risk stratification in the intermediate risk group is critical, utilising novel risk factors, genotype and multiparametric risk scores. Artificial intelligence will almost certainly play a role in diagnosis and risk stratification moving forward. Durable phenotype correction with gene therapy (or precision ablation) is an ultimate goal. This review will focus on updates in pathophysiology, diagnosis, risk stratification and management of Brugada syndrome, long QT syndrome, catecholaminergic polymorphic ventricular tachycardia and arrhythmogenic right ventricular cardiomyopathy.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".