Coronary Revascularization Versus Optimal Medical Therapy in Renal Transplant Candidates With Coronary Artery Disease: A Systematic Review and Meta-Analysis.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it