Cardiovascular Risk Factors and Comorbidities in Patients with Hyperuricemia and/or Gout: A Systematic Review of the Literature
Bibliographic record
Abstract
OBJECTIVE: To review the available literature on the likelihood of having cardiovascular (CV) risk factors and on developing CV comorbidities in patients with gout and/or asymptomatic hyperuricemia as an evidence base for generating multinational clinical practice recommendations in the 3e (Evidence, Expertise, Exchange) Initiative in Rheumatology. METHODS: A systematic literature search was carried out using MEDLINE, EMBASE, and The Cochrane Library, and abstracts presented at the 2010/2011 meetings of the American College of Rheumatology (ACR) and the European League Against Rheumatism, searching for CV risk factors and new CV comorbidities in patients with asymptomatic hyperuricemia and/or a diagnosis of gout. Trials that fulfilled predefined inclusion criteria were systematically reviewed. RESULTS: A total of 66 out of 8918 identified publications were included in this review. After assessment of the risk of bias, 32 articles with a high risk of bias were excluded. Data could not be pooled because of clinical and statistical heterogeneity. In general, both for asymptomatic hyperuricemia and for gout the hazard ratios for CV comorbidities were only modestly increased (1.5 to 2.0) as were the hazard ratios for CV risk factors, ranging from 1.4 to 2.0 for hypertension and from 1.0 to 2.4 for diabetes. CONCLUSION: Unlike the common opinion that patients with gout or hyperuricemia are at higher risk of developing CV disease, the actual risk to develop CV disease is either rather weak (for hyperuricemia) or poorly investigated (for gout).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".