A systematic review and meta-analysis of the association between uric acid levels and chronic kidney disease
Bibliographic record
Abstract
The function of uric acid (UA) in the genesis and evolution of chronic kidney disease (CKD) has motivated numerous studies, but the results remain inconclusive. We sought to conduct a systematic review and meta-analysis of cohort studies aiming to analyze the association of UA levels with the incidence and progression of CKD. Pubmed/Medline, Lilacs/Bireme and Web of Science were searched to identify eligible studies, following the PRISMA protocol. Data were presented for CKD incidence and progression separately. For the meta-analysis, studies with data stratified by subgroups according to serum UA levels were selected. The inverse variance-weighted random effects model was used to generate a combined effect estimate. Meta-regressions were performed to identify the causes of heterogeneity. The Newcastle-Ottawa Scale was used to assess the risk of bias. The publication bias was tested by funnel plot and Egger's test. Eighteen CKD incidence studies (n = 398,663) and six CKD progression studies (n = 13,575) were included. An inverse relationship was observed between UA levels and protection from CKD incidence and progression. Lower UA levels were protective for the risk of CKD incidence (RR 0.65 [95% CI 0.56-0.75]) and progression (RR 0.55 [95% CI 0.44-0.68]). UA seems to be implicated both in the genesis of CKD and its evolution.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.029 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".