Congenital Long QT Syndrome: A Focus on Risk Stratification and Management
Bibliographic record
Abstract
Congenital long QT syndrome (LQTs) is an inherited cardiac condition resulting from cardiac repolarization abnormalities. Since the initial description of congenital LQTs by Jervell and Lange-Nielsen in 1957, our understanding of this condition has increased dramatically. A diagnosis of congenital LQTs is based on the medical history of the patient, alongside electrogram features, and a genetic variant that is identified in approximately 75% of cases. The appropriate risk stratification involves a multitude of factors, with β-blockers being the cornerstone of therapy. Recent developments, such as the incorporation of artificial intelligence (AI) for electrocardiogram (ECG) interpretation, genotype-phenotype-specific therapies, and emerging gene therapies, may potentially make personalized medicine in LQTs a reality in the near future. This review summarizes our current understanding of congenital LQTs, with a focus on risk stratification, current therapeutic interventions, and emerging developments in the management of congenital LQTs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".