Sudden cardiac death in congenital heart disease
Bibliographic record
Abstract
Sudden cardiac death (SCD) accounts for up to 25% of deaths in patients with congenital heart disease (CHD). To date, research has largely been driven by observational studies and real-world experience. Drawbacks include varying definitions, incomplete taxonomy that considers SCD as a unitary diagnosis as opposed to a terminal event with diverse causes, inconsistent outcome ascertainment, and limited data granularity. Notwithstanding these constraints, identified higher-risk substrates include tetralogy of Fallot, transposition of the great arteries, cyanotic heart disease, Ebstein anomaly, and Fontan circulation. Without autopsies, it is often impossible to distinguish SCD from non-cardiac sudden deaths. Asystole and pulseless electrical activity account for a high proportion of SCDs, particularly in patients with heart failure. High-quality cardiopulmonary resuscitation is essential to improve outcomes. Pulmonary hypertension and CHD complexity are associated with lower likelihood of successful resuscitation. Risk stratification for primary prevention implantable cardioverter-defibrillators (ICDs) should consider the probability of SCD due to a shockable rhythm, competing causes of mortality, complications of ICD therapy, and associated costs. Risk scores to better estimate probabilities of SCD and CHD-specific guidelines and consensus-based recommendations have been proposed. The subcutaneous ICD has emerged as an attractive alternative to transvenous systems in those with vascular access limitations, prior device infections, intra-cardiac shunts, or a Fontan circulation. Further improving SCD-related outcomes will require a multidimensional approach to research that addresses disease processes and triggers, taxonomy to better reflect underlying pathophysiology, high-risk features, early warning signs, access to high-quality cardiopulmonary resuscitation and specialized care, and preventive therapies tailored to underlying mechanisms.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".