Outcomes with revascularization and medical therapy in patients with coronary disease and chronic kidney disease: A meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Chronic kidney disease (CKD) confers a high risk for poor cardiovascular outcomes. We conducted a systematic review and meta-analysis to estimate the effects of revascularization as the initial management strategy compared with medical therapy among patients with CKD and coronary artery disease. METHODS: or maintenance dialysis). The primary outcome was myocardial infarction. The secondary outcomes were all-cause mortality or progression to kidney failure. The risk ratio (RR) was estimated using a random-effects model. RESULTS: Eleven randomized trials were included (3422 patients). Revascularization was associated with lower incidence of myocardial infarction compared with medical therapy in patients with CKD: RR 0.71 (95% confidence interval [CI] 0.54-0.94; p=0.02). This result was mainly driven from a significantly lower incidence of myocardial infarction with early revascularization among patients with stable coronary artery disease: RR 0.59; 95% CI 0.37-0.93. A similar incidence of all-cause mortality was observed with both treatment strategies: RR 0.88 (95% CI 0.72-1.08; p=0.22). A trend towards lower incidence of all-cause mortality was observed with revascularization in the subgroup of patients presenting with NSTE-ACS: RR 0.73 (95% CI 0.51-1.04; p=0.08) but not among patients with stable coronary disease. There was no difference in progression to kidney failure between the two strategies. CONCLUSIONS: Coronary revascularization may be superior to medical therapy among patients with CKD and coronary disease.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.029 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".