Impact of hyperuricemia on long-term clinical outcomes of renal transplant recipients: a systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: To evaluate the effect of hyperuricemia on clinical outcomes of renal transplant recipients (RTRs). METHODS: A literature search of PubMed, Cochrane, Embase was conducted up to March 20, 2020. The primary outcome was the estimated glomerular filtration rate (eGFR). The second outcomes were the risk of graft loss, death, cardiovascular event and the level of triglyceride. The following search terms were utilized: ((Hyperuricemic group) OR (Hyperuricaemia) OR (Hyperuric) OR (Urea acid) OR (Uric acid) OR (Acid urate) OR (Urate) OR (Gout)) and ((Transplantation) OR (Transplantations) OR (Transplant) OR (Transplants) OR (Graft)). RESULTS: 28 studies with 18224 patients were eligible for inclusion. There was no significant difference in eGFR (<12 months, p=0.07), the risk of graft loss (<60 months, p=0.07) and death (<60months, p=0.19) between the hyperuricemic and normouricemic group in the early post-transplantation period. But increased uric acid levels contributed to the long-term decline of eGFR, the risk of graft loss and death increased after transplantation. Hyperuricemia increased the risk of cardiovascular event with no significant difference in the level of triglyceride between the two groups. CONCLUSIONS: Increased uric acid levels contributed to the long-term decline of eGFR, increased risk of graft loss and death after transplantation. Although there was no significant effect on triglyceride, hyperuricemia increased the risk of cardiovascular event.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| 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".