2921ICDs and CRTs in patients with chronic kidney disease: a meta-analysis
Bibliographic record
Abstract
Background: The efficacy of implantable cardioverter defibrillators (ICDs) and cardiac resynchronization therapy (CRT) in patients with chronic kidney disease (CKD) remains controversial despite active use. Purpose: The aim of this meta-analysis is to explore the association between ICD/CRT and survival in CKD patients. Methods: An electronic search was conducted. We included studies that reported outcomes in CKD patients stratified by the presence of ICD, CRT or none (control group). The primary outcome was all cause mortality. Outcomes were pooled using random effects model. Odds ratios (OR) were reported for dichotomous variables. Results: 10 studies (9 retrospective and 1 prospective non-randomized) including 20920 adult patients were identified. ICD was present in 3568 patients and CRT in 10838 patients. Baseline characteristics were similar between ICD, CRT and control groups. Follow up ranged between 1–3 years. All cause mortality was lower in the ICD group in comparison to control group (51% vs. 66%, OR 0.48 (95% confidence interval [CI] 0.42; 0.55), P<0.001). All cause mortality was lower in CRT vs. ICD groups (37% vs. 42%, OR 1.38 (95% CI 1.08; 1.76), P=0.01). No significant heterogeneity was noted for the comparisons (I2=0%, P=0.51 and I2=31%, P=0.23, respectively).
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.059 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".