Efficacy and Tolerability of Probenecid as Urate-lowering Therapy in Gout; Clinical Experience in High-prevalence Population
Bibliographic record
Abstract
OBJECTIVE: Probenecid is recommended as urate-lowering therapy (ULT) in patients with gout where xanthine oxidase inhibitors are ineffective, not tolerated, or contraindicated. The aim of our study was to determine the efficacy of probenecid to achieve serum urate (SU) targets (< 0.36 mmol/l) in clinical practice. METHODS: We identified 57 patients prescribed with probenecid from a database of 521 rheumatology clinic attenders with gout. Demographic characteristics, indications for probenecid, probenecid doses, side effects, and laboratory data including estimated glomerular filtration rate (eGFR) and SU were recorded. RESULTS: There were 30/57 (53%) patients treated with probenecid as monotherapy and 27/57 (47%) patients treated with probenecid in combination with allopurinol. Target SU concentrations (< 0.36 mmol/l) were achieved in 10/30 (33%) of the probenecid monotherapy group and 10/27 (37%) of the combination treatment group. Baseline SU concentrations, but not eGFR or probenecid dose, independently predicted achievement of target SU. Target SU was achieved in 5/15 (33%) patients with eGFR < 50 ml/min/1.73 m(2). There was no difference in the percentage of patients achieving SU target in those with eGFR < 50 ml/min/1.73 m(2) compared with those with eGFR ≥ 50 ml/min/1.73 m(2). Adverse events attributed to probenecid were observed in 8/42 (19%) patients with eGFR ≥ 50 ml/min/1.73 m(2) and in 2/15 (13%) patients with eGFR < 50 ml/min/1.73 m(2). CONCLUSION: Probenecid has moderate efficacy as ULT in clinical management of patients with complex gout who have a lack of efficacy or intolerance to allopurinol. Patients with chronic kidney disease may respond to probenecid with similar rates of adverse events.
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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 | 0.001 |
| 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 | 0.000 |
| 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".