Coronary artery bypass grafting versus percutaneous coronary intervention in <scp>end‐stage</scp> kidney disease: A systematic review and <scp>meta‐analysis</scp> of clinical studies
Bibliographic record
Abstract
The most significant complication of end-stage kidney disease (ESKD) is cardiovascular disease, mainly coronary artery disease (CAD). Although the effective treatment of CAD is an important prognostic factor, whether percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) is better for treating CAD in this group of patients is still controversial. We searched Pubmed/Medline, Web of Science, Embase, the Cochrane Central Register of Controlled Trials articles that compared the outcomes of CABG versus PCI in patients with ESKD requiring dialysis. A total of 10 observational studies with 39,666 patients were included. Our analysis showed that when compared to PCI, CABG had lower risk of need for repeat revascularization (relative risk [RR] = 2.25, 95% confidence interval [CI] 2.1-2.42, p < 0.00001) and cardiovascular death (RR = 1.19, 95% CI 1.14-1.23, p < 0.00001) and higher risk for short-term mortality (RR = 0.43, 95% CI 0.38-0.48, p < 0.00001). There was no statistically significant difference between the PCI and CABG groups in the risk for late mortality (RR = 1.05, 95% CI 0.97-1.14, p = 0.25), myocardial infarction (RR = 1.05, 95% CI 0.46-2.36, p = 0.91) or stroke (RR = 1.02, 95% CI 0.64-1.61, p = 0.95). This meta-analysis showed that in ESKD patients requiring dialysis, CABG was superior to PCI in regard to cardiovascular death and need for repeat revascularization and inferior to PCI in regard to short term mortality. However, this meta-analysis has limitations and needs confirmation with large randomized controlled trials.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".