Bibliographic record
Abstract
PURPOSE OF REVIEW: Clinical trials provide evidence that an empirical approach of implantable cardioverter-defibrillator implantation in all heart failure patients (ejection fraction </= 35%) with mild to moderate symptoms effectively reduces mortality rate as compared to the best available medical therapy. At least 50% of patients, however, will succumb to a non-arrhythmic demise and over half of all patients will not require device therapy over long-term follow-up. Thus, the approach of empiric implantable cardioverter-defibrillator implantation is costly in light of the considerable expense of device cost, implantation, and patient follow-up. This review discusses the prospect of genomic medicine as an approach to assess genetic susceptibility to sudden arrhythmic death in at-risk populations. RECENT FINDINGS: Through the past 10 years of primary prevention implantable cardioverter-defibrillator trials, the number of patients needed to treat to prevent a sudden death has risen from one in four patients to one in 14. Although numerous clinical tests exist for stratification, they are of low positive predictive value in assessing arrhythmic risk, and have not been prospectively validated as effective strategies in identifying arrhythmia-prone patients. Recent data from genetic studies identifying genes responsible for sudden cardiac death have compiled a list of relatively common genetic variations (polymorphisms). These polymorphisms, encoding for proteins known to be involved in cardiac electrophysiology, may contribute to arrhythmic risk in the milieu of heart failure. SUMMARY: Current clinical indications for implantable cardioverter-defibrillator implantation in primary prophylaxis of sudden cardiac death necessarily include a significant number of patients who may not benefit. The identification of common genetic variations causing an increased risk of vulnerability to ventricular arrhythmia in heart failure patients may optimize the use of medical resources through rapid identification of sub-populations at highest risk.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".