Management of Gout in a Hospital Setting: A Lost Opportunity
Bibliographic record
Abstract
OBJECTIVE: Management of gout is frequently suboptimal. The aim of this study was to determine the proportion of patients presenting to Christchurch Hospital for a gout flare and to determine whether management for both acute flares and urate lowering was in accordance with international recommendations. METHODS: A retrospective audit was undertaken of all admissions to Christchurch Hospital from June 1, 2013, to May 31, 2014, in which gout was coded as a primary or secondary discharge diagnosis. Information including demographics, comorbidities, concomitant medications, treatment of acute gout, and urate lowering was collected. RESULTS: A total of 235 acute admissions for gout in 216 individuals were identified. Eleven individuals had 2 admissions and 4 individuals had 3 admissions. In 95/235 admissions (40.4%), gout was the primary diagnosis. Gout accounted for 95/77,321 (0.12%) of acute admissions. The treatment of acute gout was prednisone monotherapy in 170/235 (72.3%) of admissions. Serum urate was measured at some point during 123/235 (52.3%) of admissions, with only 19/123 (15.4%) at target urate level (< 0.36 mmol/l). At 60 of the 235 admissions, urate-lowering therapy was already being prescribed. Nine out of 175 patients (5.1%) not treated with urate-lowering therapy at admission commenced allopurinol and 32/174 (18.4%) had commencement of urate-lowering therapy recommended in the discharge plan. CONCLUSION: Rates of admission for gout are similar to that observed in other studies. Failure to initiate, change, or recommend alterations in urate-lowering therapy to achieve target urate in people with gout admitted to hospital represents a significant lost opportunity to improve longterm 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".