Predictors for transplant renal artery stenosis in kidney transplant recipients: a systematic-review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Kidney transplant recipients after kidney transplantation may develop transplant renal artery stenosis (TRAS). Multiple studies have sought to identify risk factors, yet the findings remain inconsistent. METHODS: PubMed, Scopus, and Web of Science Core Collection were comprehensively searched to retrieve studies. The 1st screening phase required studies to be in English and evaluate patients with TRAS. The 2nd screening phase needed studies to provide extractable data. Quality of each included study was assessed using the Newcastle-Ottawa Scale. The protocol was registered through PROSPERO (CRD42023455295). RESULTS: A total of 25 cohort and 6 case-control studies were included, with the majority being of moderate quality. The predictors for TRAS identified in univariate analysis were deceased donor (odds ratio (OR), 1.74; p = 0.04), delayed graft function (OR 2.46; p = 0.0004), acute rejection (OR 2.03; p = 0.001), prolonged cold ischemia time (p = 0.01), multiple renal arteries (OR 1.73; p = 0.03), right kidney implantation (OR 1.89; p = 0.005), ischemic heart disease (OR 1.64; p = 0.0004), diabetes mellitus (OR 1.58; p = 0.001), hypertension (OR 1.28; p = 0.0009), immunosuppression with mycophenolate mofetil (OR 1.22; p = 0.02), cytomegalovirus status (OR 1.94; p 0.0001) and older recipient age (p = 0.01). CONCLUSIONS: This study provides the largest and most reliable review on predictors for TRAS following kidney transplantation. Despite the chance of some factors being dependent of one another, our findings would still be helpful for clinicians in risk-stratification for TRAS and long-term follow-up of kidney transplant recipients.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".