Role of risk stratification and genetics in sudden cardiac death
Bibliographic record
Abstract
Sudden cardiac death (SCD) is a major public health issue due to its increasing incidence in the general population and the difficulty in identifying high-risk individuals. Nearly 300 000 - 350 000 patients in the United States and 4-5 million patients in the world die annually from SCD. Coronary artery disease and advanced heart failure are the main etiology for SCD. Ischemia of any cause precipitates lethal arrhythmias, and ventricular tachycardia and ventricular fibrillation are the most common lethal arrhythmias precipitating SCD. Pulseless electrical activity, bradyarrhythmia, and electromechanical dissociation also result in SCD. Most SCDs occur outside of the hospital setting, so it is difficult to estimate the public burden, which results in overestimating the incidence of SCD. The insufficiency and limited predictive value of various indicators and criteria for SCD result in the increasing incidence. As a result, there is a need to develop better risk stratification criteria and find modifiable variables to decrease the incidence. Primary and secondary prevention and treatment of SCD need further research. This critical review is focused on the etiology, risk factors, prognostic factors, and importance of risk stratification of SCD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".