Factors associated with the development of cardiac allograft vasculopathy – a systematic review of observational studies
Bibliographic record
Abstract
Cardiac allograft vasculopathy (CAV) is a significant factor impacting outcomes after heart transplant. We performed a systematic review of risk factors for the development of CAV. A search of electronic databases was performed. The eligibility criteria included cohort and case-control studies with more than 50 adult patients submitted to a heart transplant. The outcome should be CAV diagnosed by angiography and/or intravascular ultrasound (IVUS). Two reviewers performed study selection, data abstraction, and quality assessment. Of 2514 citations, 66 articles were included--46 had 200 participants or less; 61 were single-center; and 44 were retrospective cohorts. The most used definition of CAV using angiography was the detection of any degree of abnormality (21 studies of 58). In studies using IVUS, an intimal thickness ≥0.5 mm was the most used definition (five of eight studies). Quality assessment revealed an inadequate description of patient selection, attrition, and accounting of potential confounders. Donor age, recipient age, recipient gender, etiology of heart failure, ischemic time, human leukocyte antigen matching, cytomegalovirus, lipid profile, and rejection episodes were the most studied factors. Our review indicates that the current evidence is not consistent across different studies. The definite contribution of risk factors for the development of CAV is still to be determined.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".