Serum Urate and Incidence of Kidney Disease Among Veterans with Gout
Bibliographic record
Abstract
OBJECTIVE: To study the association between serum urate level (sUA) and the risk of incident kidney disease among US veterans with gouty arthritis. METHODS: From 2002 through 2011 adult male patients with gout who were free of kidney disease were identified in the data from the Veterans Administration VISN 16 database and were followed until incidence of kidney disease, death, or the last available observation. Accumulated hazard curves for time to kidney disease were estimated for patients with average sUA levels > 7 mg/dl (high) versus ≤ 7 mg/dl (low) based on Kaplan-Meier analyses; and statistical comparison was conducted using a log-rank test. A Cox proportional hazard model with time-varying covariates was used to estimate the unadjusted and adjusted hazard ratios for kidney disease. RESULTS: Eligible patients (n = 2116) were mostly white (53%), with average age 62.6 years, mean body mass index 31.2 kg/m(2), and high baseline prevalence of hypertension (93%), hyperlipidemia (67%), and diabetes (20%). Mean followup time was 6.5 years. The estimated rates of all incident kidney disease in the overall low versus high sUA groups were 2% versus 4% at Year 1, 3% versus 6% at Year 2, and 5% versus 9% at Year 3, respectively (p < 0.0001). After adjustment, high sUA continued to predict a significantly higher risk of kidney disease development (HR 1.43, 95% CI 1.20-1.70). CONCLUSION: Male veterans with gout and sUA levels > 7 mg/dl had an increased incidence of kidney disease.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".