Treatment of Asymptomatic Hyperuricemia and Prevention of Vascular Disease: A Decision Analytic Approach
Bibliographic record
Abstract
OBJECTIVE: Elevated serum urate may be associated with an increase in cardiovascular (CV) disease. Treating asymptomatic hyperuricemia with urate-lowering drugs such as allopurinol may reduce CV events. We designed a model to simulate the effect of allopurinol treatment on reducing frequency of CV events in individuals with elevated serum urate. METHODS: A Markov state-transition model was constructed to assess occurrence of vascular events (VE) for 2 treatment strategies: treat all asymptomatic individuals with allopurinol (Treat All) and treat only if symptomatic (Treat Symptomatic). The model simulated a hypothetical cohort of 50-year-old men with different serum urate concentrations (6-6.9 and 7-7.9 mg/dl) followed over 20 years. Age and sex subgroups were analyzed. Model inputs were derived from current literature. The main outcome measures were mean number of VE and mean number of deaths from VE. RESULTS: For 50-year-old men with serum urate 6.0-6.9 mg/dl, individuals in the Treat All strategy have a 30% reduction in the mean number of VE compared to those in the Treat Symptomatic strategy (mean VE: 0.078 vs 0.11), and a 39% reduction in mean number of deaths from VE. At higher serum urate concentrations, treatment is more effective in reducing the mean number of VE and mean number of deaths from VE (38% event, 54% death). Results for women show similar trends. As the cohort ages, treatment has less effect on reducing VE. The number needed to treat to prevent 1 event is 20 (men, 7.0-7.9 mg/dl). CONCLUSION: The model predicts that treating asymptomatic hyperuricemia with allopurinol is most effective in preventing VE at a serum urate above 7.0 mg/dl in men and 5.0 mg/dl in women.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".