Sudden Cardiac Death Risk Stratification
Bibliographic record
Abstract
Arrhythmic sudden cardiac death (SCD) may be caused by ventricular tachycardia/fibrillation or pulseless electric activity/asystole. Effective risk stratification to identify patients at risk of arrhythmic SCD is essential for targeting our healthcare and research resources to tackle this important public health issue. Although our understanding of SCD because of pulseless electric activity/asystole is growing, the overwhelming majority of research in risk stratification has focused on SCD-ventricular tachycardia/ventricular fibrillation. This review focuses on existing and novel risk stratification tools for SCD-ventricular tachycardia/ventricular fibrillation. For patients with left ventricular dysfunction or myocardial infarction, advances in imaging, measures of cardiac autonomic function, and measures of repolarization have shown considerable promise in refining risk. Yet the majority of SCD-ventricular tachycardia/ventricular fibrillation occurs in patients without known cardiac disease. Biomarkers and novel imaging techniques may provide further risk stratification in the general population beyond traditional risk stratification for coronary artery disease alone. Despite these advances, significant challenges in risk stratification remain that must be overcome before a meaningful impact on SCD can be realized.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".