Coronary Revascularization Versus Optimal Medical Therapy in Renal Transplant Candidates With Coronary Artery Disease: A Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
Background Coronary artery disease (CAD) is highly prevalent in patients with chronic kidney disease and is a common cause of mortality in end-stage renal disease. Thus, patients with end-stage renal disease are routinely screened for CAD before renal transplantation. The usefulness of revascularization before transplantation remains unclear. We hypothesize that there is no difference in all-cause and cardiovascular mortality in waitlisted renal transplant candidates with CAD who underwent revascularization versus those treated with optimal medical therapy before transplantation. Methods and Results This meta-analysis was reported according to the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines. MEDLINE, Scopus, and Cochrane Central Register of Controlled Trials were systematically searched to identify relevant studies. Risk of bias was assessed using the modified Newcastle-Ottawa Scale and Cochrane risk of bias tool. The primary outcome of interest was all-cause mortality. Eight studies comprising 945 patients were included (36% women, mean age 56 years). There was no difference in all-cause mortality (risk ratio [RR], 1.16 [95% CI, 0.63-2.12), cardiovascular mortality (RR, 0.75 [95% CI, 0.29-1.89]), or major adverse cardiovascular events (RR, 0.78 [95% CI, 0.30-2.07]) when comparing renal transplant candidates with CAD who underwent revascularization versus those who were on optimal medical therapy before renal transplant. Conclusions This meta-analysis demonstrates that revascularization is not superior to optimal medical therapy in reducing all-cause mortality, cardiovascular mortality, or major adverse cardiovascular events in waitlisted kidney transplant candidates with CAD who eventually underwent kidney transplantation.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".