[The correlation between uric acid and the incidence and prognosis of kidney diseases: a systematic review and meta-analysis of cohort studies].
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association of serum uric acid and the incidence and prognosis of kidney diseases systematically, so as to provide reference for the treatment and prevention of kidney diseases. METHODS: Literatures related to the associations between serum uric acid and incidence and prognosis of kidney diseases were selected from the Chinese Biomedical Literature Database (CBM) (January 1982 to March 2010), EMBASE (January 1966 to March 2010) and Medline (January 1950 to March 2010) for cohort studies. Two researchers independently screened the studies, assessed the risk of bias of included studies using the Newcastle-Ottawa Quality Assessment Scale for Cohort Studies and extracted data. Stata 10.0 was used to calculate the pooled relative risk. RESULTS: Twenty-one eligible cohort studies were selected, of which 11 on incidence of kidney diseases (n = 276 801), and 10 on the prognosis of kidney diseases (n = 3004). Meta analysis was performed based on data influencing incidence and prognosis factors of kidney diseases except for serum uric acid. The results showed, (1) uric acid and incidence of kidney diseases: hyperuricemia could increase the risk of kidney diseases (RR = 1.49, 95%CI 1.27 - 1.75); (2) uric acid and prognosis of patients with kidney diseases:hyperuricemia could deteriorate the kidney function (RR = 1.35, 95%CI 1.12 - 1.63) and increase the risk of mortality (RR = 1.67, 95%CI 1.29 - 2.16). CONCLUSION: Uric acid is an independent risk factor for incidence of kidney diseases and poor prognosis of patients with kidney diseases. Further high quality clinical trials with long-term follow up should be conducted to determine whether lowering uric acid levels would be of clinical benefit in the prevention and treatment of kidney diseases, so as to provide direct evidence for clarifying correlation between uric acid and kidney diseases and the prevention and treatment for patients with high uric acid.
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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 | 0.024 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".