Hyperuricemia and risk of stroke: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To assess the association between hyperuricemia and risk of stroke incidence and mortality because hyperuricemia is hypothesized to be a risk factor for stroke and other cardiovascular disease, but, to date, results from observational studies are conflicting. METHODS: A systematic review and meta-analysis were conducted. Studies were identified by searching major electronic databases using the Medical Subject Headings and keywords without restriction in languages. Prospective cohort studies were included only if they contained data on stroke incidences or mortalities related to serum uric acid levels in adults. Pooled risk ratios (RRs) for the association of stroke incidence and mortality with serum uric acid levels were calculated. RESULTS: A total of 16 studies including 238,449 adults were eligible and abstracted. Hyperuricemia was associated with a significantly higher risk of both stroke incidence (6 studies; RR 1.41, 95% confidence interval [95% CI] 1.05, 1.76) and mortality (6 studies; RR 1.36, 95% CI 1.03, 1.69) in our meta-analyses of unadjusted study estimates. Subgroup analyses of studies adjusting for known risk factors such as age, hypertension, diabetes mellitus, and cholesterol still showed that hyperuricemia was significantly associated with both stroke incidence (4 studies; RR 1.47, 95% CI 1.19, 1.76) and mortality (6 studies; RR 1.26, 95% CI 1.12, 1.39). The pooled estimate of multivariate RRs did not differ significantly by sex. CONCLUSION: Hyperuricemia may modestly increase the risks of both stroke incidence and mortality. Future research is needed to determine whether lowering uric acid level has any beneficial effects on stroke.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".