3295ICD therapy in primary prevention with mid-range LVEF in the painFree SST Study
Bibliographic record
Abstract
Background: ESC guidelines recommend an implantable cardioverter defibrillator (ICD) for primary prevention of sudden cardiac death in patients with a history of myocardial infarction (MI) and reduced left ventricular ejection fraction (LVEF). However, evidence of benefit of ICD in patients with LVEF >30% is limited. Method: This sub-analysis of the PainFree SST trial evaluates ICD therapy in primary prevention patients with prior MI, comparing LVEF ≤30% versus >30%. Results: A total of 686 primary prevention patients with a history of MI were included, of which 505 had LVEF ≤30% and 181 had LVEF >30%. In the LVEF >30% group, 87.3% of the patients had LVEF ≤40%. All devices included advanced shock reduction features and 80.4% had prolonged detection programming. At 24 months post-implant, the incidence of appropriate ICD shock was similar among the two groups (7.9% in LVEF ≤30% patients vs. 5.5% in LVEF >30% patients, HR = 0.79, 95% CI: 0.39–1.60, p=0.50, Figure). The incidence of appropriate ATP was also not significantly different among the two groups (10.7% in LVEF ≤30% patients vs. 5.4% in LVEF >30% patients, HR = 0.48 95% CI: 0.23–1.03, p=0.055). No significant differences were observed with regards to delivery of inappropriate therapy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.005 | 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".