Effects of uric acid-lowering therapy on renal outcomes: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Non-randomized studies suggest an association between serum uric acid levels and progression of chronic kidney disease (CKD). The aim of this systematic review is to summarize evidence from randomized controlled trials (RCTs) concerning the benefits and risks of uric acid-lowering therapy on renal outcomes. METHODS: Medline, Excerpta Medical Database and Cochrane Central Register of Controlled Trials were searched with English language restriction for RCTs comparing the effect of uric acid-lowering therapy with placebo/no treatment on renal outcomes. Treatment effects were summarized using random-effects meta-analysis. RESULTS: Eight trials (476 participants) evaluating allopurinol treatment were eligible for inclusion. There was substantial heterogeneity in baseline kidney function, cause of CKD and duration of follow-up across these studies. In five trials, there was no significant difference in change in glomerular filtration rate from baseline between the allopurinol and control arms [mean difference (MD) 3.1 mL/min/1.73 m2, 95% confidence intervals (CI) -0.9, 7.1; heterogeneity χ2=1.9, I2=0%, P=0.75]. In three trials, allopurinol treatment abrogated increases in serum creatinine from baseline (MD -0.4 mg/dL, 95% CI -0.8, -0.0 mg/dL; heterogeneity χ2=3, I2=34%, P=0.22). Allopurinol had no effect on proteinuria and blood pressure. Data for effects of allopurinol therapy on progression to end-stage kidney disease and death were scant. Allopurinol had uncertain effects on the risks of adverse events. CONCLUSIONS: Uric acid-lowering therapy with allopurinol may retard the progression of CKD. However, adequately powered randomized trials are required to evaluate the benefits and risks of uric acid-lowering therapy in CKD.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".