Effectivity and Safety of Febuxostat in Reducing Serum Urate in Gout Patients With Chronic Kidney Disease: A Prospective Multicenter ULTRA Registry Study
Bibliographic record
Abstract
Objective Gout, an inflammatory arthritis caused by hyperuricemia, is highly prevalent with chronic kidney disease (CKD). We evaluated longitudinal changes in serum urate (SU) levels and febuxostat dosage according to renal function. Methods Among 405 patients in the Urate-Lowering Therapy in Gout (ULTRA) registry between November 2021 and December 2023, 112 were analyzed after excluding those with < 1-year follow-up period, nonfebuxostat therapy, or missing data. SU levels and febuxostat doses were compared between the 2 groups at baseline, 6, and 12 months. Results Baseline SU levels did not differ between the normal and CKD groups. After febuxostat therapy, mean (SD) SU levels were significantly lower in the CKD group than in the normal group (at 6 months: 4.45 [1.84] mg/dL vs 5.62 [1.62] mg/dL, P = 0.001; at 12 months: 4.81 [1.81] mg/dL vs 5.60 [1.94] mg/dL, P = 0.04). Meanwhile, the mean dosages of febuxostat were lower in CKD group than in the normal group (at 6 months: 40.61 [22.07] mg vs 47.54 [19.43] mg, P = 0.11; at 12 months: 40.59 [21.73] mg vs 48.49 [19.70] mg, P = 0.06), although these differences were not statistically significant. Additionally, the proportion of patients achieving SU < 6 mg/dL at 6 months was higher in the CKD group than in the normal group (91.2% vs 68.6%, P = 0.01). Conclusion An individualized dosing strategy based on SU response, rather than renal function alone, may optimize treatment outcomes in these patients.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".