Implantable Defibrillators Improve Survival in End-Stage Renal Disease: Results from a Multi-Center Registry
Bibliographic record
Abstract
BACKGROUND: Small retrospective analyses suggest that end-stage renal disease (ESRD) patients do not obtain as much of a survival benefit from an implantable cardioverter-defibrillator (ICD) as non-ESRD patients do. We aimed to assess the survival effect of an ICD in ESRD patients with left ventricular dysfunction. METHODS: Data from two registries identified ESRD patients with an ICD and ESRD patients with left ventricular dysfunction (defined as ejection fraction <0.35). Cox proportional hazards regression was performed, including certain predefined covariates to assess the effect of an ICD on survival. RESULTS: Overall survival in the full cohort was a median of 4.7 years with 20 deaths in the ICD group and 29 deaths in the no-ICD group. The median survival in the ICD group was 8.0 years and 3.1 years in the no-ICD group. Crude analysis showed a better survival in the ICD group as compared to the no-ICD group (p = 0.016). The multivariable analysis confirmed that the ICD group had significantly less all-cause mortality compared to the no-ICD group (HR: 0.40; 95% CI: 0.19, 0.82; p = 0.013). CONCLUSION: An ICD is associated with a higher survival in ESRD patients with left ventricular dysfunction. This result merits further study in a larger cohort of patients.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".