Clinical Efficacy and Safety of Successful Longterm Urate Lowering with Febuxostat or Allopurinol in Subjects with Gout
Bibliographic record
Abstract
OBJECTIVE: To determine longterm urate-lowering efficacy and clinical benefits and safety of therapy with febuxostat or allopurinol in subjects with gout. METHODS: Subjects (n=1086) in this open-label extension study were assigned to fixed-dose daily urate-lowering treatment (ULT) with febuxostat (80 mg or 120 mg) or allopurinol (300 mg). ULT reassignment was permitted during months 1 to 6 to achieve serum urate (SUA) concentrations between 3.0 and <6.0 mg/dl. Flares requiring treatment, tophus size, safety, and SUA levels were monitored during up to 40 months of ULT maintenance. RESULTS: After 1 month initial treatment, >80% of subjects receiving either febuxostat dose, but only 46% of subjects receiving allopurinol, achieved SUA<6.0 mg/dl. After ULT reassignment, >80% of all remaining subjects maintained the primary efficacy endpoint of SUA<6.0 mg/dl at each visit. More subjects initially randomized to allopurinol required ULT reassignment to achieve SUA<6.0 mg/dl compared with subjects receiving febuxostat. Maintenance of SUA<6.0 mg/dl resulted in progressive reduction to nearly 0 in proportion of subjects requiring gout flare treatment. Baseline tophus resolution was achieved by 46%, 36%, and 29% of subjects maintained on febuxostat 80 mg, febuxostat 120 mg, and allopurinol, respectively. Overall adverse event rates (including cardiovascular adverse event rates), adjusted for 10-fold greater febuxostat than allopurinol exposure, did not differ significantly among treatment groups. CONCLUSION: Durable maintenance of goal range SUA level with either dose of febuxostat or in smaller numbers of subjects with allopurinol resulted in near elimination of gout flares and improved tophus status over time. Registered as NCT00175019.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".