Coronary Artery Revascularization in Heart Transplant Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Cardiac allograft vasculopathy (CAV) is the primary cause of late mortality after heart transplantation. We look to provide a comprehensive review of contemporary revascularization strategies in CAV. METHODS: PubMed and Web of Science were systematically searched by 3 authors. 1,870 articles were initially screened and 24 were included in this review. RESULTS: PCI is the main revascularization technique utilized in CAV. The pooled estimates for restenosis significantly favored DES over BMS (OR 4.26; 95% CI: 2.54-7.13; p < 0.00001; I2 = 4%). There were insufficient data to quantitatively compare mortality following DES versus BMS. There was no difference in short-term mortality between CABG and PCI. In-hospital mortality was 0.0% for CABG and ranged from 0.0 to 8.34% for PCI. One-year mortality was 8.0% for CABG and 5.0-25.0% for PCI. CABG had a potential advantage at 5 years. Five-year mortality was 17.0% for CABG and ranged from 14 to 40.4% following PCI. Select measures of postoperative morbidity trended toward superior outcomes for CABG. CONCLUSION: In CAV, PCI is the primary revascularization strategy utilized, with DES exhibiting superiority to BMS regarding postoperative morbidity. Further investigation into outcomes following CABG in CAV is required to conclusively elucidate the superior management strategy in CAV.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".