Serum uric acid and risk of cardiovascular mortality: a systematic review and dose-response meta-analysis of cohort studies of over a million participants
Bibliographic record
Abstract
Abstract Background Cardiovascular disease (CVD) is the leading cause of death worldwide. Some studies have suggested anassociation between serum uric acid levels and cardiovascular mortality; however, the results have not been summarized in a meta-analysis. Methods A comprehensive search of all related studies until April 2018was performed in MEDLINE/PubMed and Scopus databases DerSimonianand Laird random-effects models were used to combine hazard ratios (HRs) with 95% confidence intervals (CIs). Dose-response analysis was also carried out. Results Thirty-two studies containing forty-four arms with 1,134,073 participants reported association between uric acid and risk of CVD mortality were included in our analysis. Pooled results showed a significant positive association between uric acid levels and risk of CVD mortality (HR 1.45, 95% CI 1.33–1.58, I 2 = 79%). Sub-group analysis showed this relationshipwasstronger in women compared to men. Moreover, there was a significant non-linear association between uric acid levels and the risk of CVD mortality (r = 0.0709, p = 0.001). Conclusion Our analysis indicates a positive dose-response association between SUA and CVD mortality risk.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.024 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".