Do patients at high risk of nonsudden cardiac death benefit from prophylactic ICD therapy?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Randomized controlled trials have established that prophylactic implantable cardioverter defibrillator (ICD) therapy improves survival in patients with reduced left ventricular ejection fraction (LVEF). However, mortality reduction is not uniform across the implanted population and recent data have highlighted the importance of nonsudden cardiac death (non-SCD) risk in predicting benefit from ICD therapy. This review explores the importance of non-SCD risk in patient selection for prophylactic ICD therapy, as well as the proposed approaches to identify potential ICD recipients at high risk of non-SCD. RECENT FINDINGS: Data from randomized controlled trials have demonstrated that patients at high risk of non-SCD do not gain significant survival benefit from prophylactic ICD therapy irrespective of their risk of SCD. A variety of strategies to identify low LVEF patients at high risk of non-SCD have been proposed. These include the use of individual risk markers, such as advanced age and renal dysfunction, the presence of cardiac and noncardiac comorbidities, and the use of more complex risk scores. SUMMARY: Non-SCD risk is an important issue in patient selection for prophylactic ICD therapy. However, the optimal strategy to identify patients at high non-SCD risk is unclear and further research is needed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 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".