Allopurinol and mortality in hyperuricaemic patients
Bibliographic record
Abstract
OBJECTIVES: While studies have suggested that gout and hyperuricaemia are associated with the risk of premature death, none has investigated the role of urate-lowering therapy on this critical outcome. We examined the impact of allopurinol, the most commonly used urate-lowering drug, on the risk of mortality in hyperuricaemic patients. METHODS: From a population of hyperuricaemic veterans of [serum urate level >416 micromol/l (7.0 mg/dl)] at least 40 years of age, we compared the risk of death between incident allopurinol users (n = 2483) and non-users (n = 7441). We estimated the multivariate mortality hazard ratio (HR) of allopurinol use with Cox proportional hazards models. RESULTS: Of the 9924 veterans (males, 98% and mean age 62.7 years), 1021 died during the follow-up. Patients who began treatment with allopurinol had worse prognostic factors for mortality, including higher BMI and comorbidities. After adjusting for baseline urate levels, allopurinol treatment was associated with a lower risk of all-cause mortality (HR 0.78; 95% CI 0.67, 0.91). Further adjustment with other prognostic factors did not appreciably alter this estimate (HR 0.77; 95% CI 0.65, 0.91). The mean change from baseline in serum urate within the allopurinol group was -111 micromol/l (-1.86 mg/dl). Adjusting for baseline urate level, allopurinol users had a 40 micromol/l (0.68 mg/dl) lower follow-up serum urate value than controls (95% CI -0.55, -0.81). CONCLUSION: Our findings indicate that allopurinol treatment may provide a survival benefit among patients with hyperuricaemia.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".