Inpatient Management of Gout: Serum Urate Testing and Allopurinol Dose Adjustment
Bibliographic record
Abstract
Objective Despite effective treatment, gout is poorly managed. The aim of this study was to determine rates of serum urate (SU) testing and allopurinol dose adjustment in patients admitted to Christchurch-based hospitals who were receiving allopurinol. Methods The hospital electronic prescribing and administration (ePA) system was used to identify patients receiving allopurinol during hospital admissions from March 2016 to March 2023. Demographics, SU, renal function, and changes to allopurinol therapy were recorded for each admission. Results were stratified by target SU and renal function. Results Of 18,081 patients who received allopurinol, SU was measured in 2950 (16.32%). The mean SU was 0.37 (SD 0.12) mmol/L, with 1270 (43.05%) above target SU (0.36 mmol/L). Admissions with chronic kidney disease (CKD) stage 3-5 were more likely to have SU above target than those with CKD stage 1-2 (78.84% vs 21.26%;P< 0.001). Among those with SU above target, allopurinol was discontinued in 148 (11.65%) and the dose reduced in 44 (3.46%), increased in 92 (7.24%), and unchanged in 986 (77.63%) during the admission. Those above target SU with CKD stage 3-5 were more likely to stop or decrease allopurinol compared to those with CKD stage 1-2 (16.4% vs 10.4%;P= 0.01). Conclusion More than 80% of hospital admissions did not have SU measured, despite the patient receiving allopurinol. Most admissions had suboptimal management of the allopurinol dose in the context of their SU. These results reflect a missed opportunity to review and optimize gout management.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".