Frequency, Risk, and Cost of Gout-related Episodes Among the Elderly: Does Serum Uric Acid Level Matter?
Bibliographic record
Abstract
OBJECTIVE: We examined the association between serum uric acid (SUA) level and the frequency, risk, and cost of gout flares among the elderly. METHODS: Data were extracted from the Integrated Healthcare Information Services claims database (1999-2005). Patients were included if they had gout, were aged 65 years and older and had both medical and pharmacy benefits, and electronic laboratory data. Patients with gout and gouty episodes were identified using algorithms based on ICD-9-CM codes and medications. Logistic regression and negative binomial regressions were used to study the relationship between SUA concentration and the annual frequency and one-year risk of gout episodes. Generalized linear models were used to examine the direct healthcare costs associated with gout episodes in the 30 days following each episode. RESULTS: Elderly patients with gout (n = 2237) with high (6-8.99 mg/dl) and very high (> 9 mg/dl) SUA concentrations were more likely to develop a flare within 12 months compared to patients with normal (< 6 mg/dl) SUA levels (OR 2.1, 95% CI 1.7-2.6; OR 3.4, 95% CI 2.6-4.4, respectively). In multivariate regressions, the average annual number of flares increased by 11.9% (p < 0.001) with each unit-increase in SUA level above 6 mg/dl (p < 0.001). Among patients with very high SUA levels, average adjusted total healthcare and gout-related costs per episode were $2,555 and $356 higher, respectively, than those of patients with normal SUA levels (both p < 0.001). CONCLUSION: Higher SUA levels are associated with increased frequency and risk of gout episode, and with higher total and gout-related direct healthcare costs per episode.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".