Prevalence of Comorbid Conditions and Prescription Medication Use Among Patients With Gout and Hyperuricemia in a Managed Care Setting
Bibliographic record
Abstract
BACKGROUND: : Comorbid disorders and multiple prescription drug use are common among patients with gout and/or hyperuricemia and may influence the clinical course and outcome of gout. OBJECTIVE: : We wanted to document the conditions and associated medications in a large group of patients with gout in a managed care setting. METHODS: : This study was a 2-year, retrospective, administrative claims analysis examining comorbid conditions and medication use among managed care enrollees with gout/hyperuricemia across the United States. RESULTS: : Of the 9482 study subjects (82.1% men, mean age 52 years), 57.9% had hypertension, 45.3% had a lipid disorder, 32.5% had both conditions, and 19.9% had diabetes mellitus. During the 24-month follow-up period, subjects had 5 +/- 3.14 (mean +/- standard deviation) different comorbid conditions and filled prescriptions for of 11.0 +/- 7.90 different medications. The most commonly filled prescriptions included antihypertensive drugs, 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase inhibitors (statins), and nonsteroidal antiinflammatory drugs (NSAIDs). CONCLUSIONS: : The study indicates a high prevalence of both comorbid conditions and multiple medication use among managed care enrollees with gout and/or hyperuricemia. Heightened awareness of these associated disorders is important because they may warrant treatment of their own accord and often some modification of gout management. Drugs, particularly diuretics and prophylactic aspirin, could potentially contribute to the development of hyperuricemia and 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".