Impact of a Rheumatology Consultation Service on the Diagnostic Accuracy and Management of Gout in Hospitalized Patients
Bibliographic record
Abstract
OBJECTIVE: To determine if a hospital rheumatology consultation service improves diagnostic accuracy and adherence to treatment recommendations for gout. METHODS: This was a retrospective, single-center, case-control study of consecutive hospitalized patients with gout. Demographic, diagnostic, and treatment variables were compared in patients with and without a rheumatology consultation (controls). American College of Rheumatology (ACR) preliminary criteria for the classification of acute gout and the European League Against Rheumatism (EULAR) recommendations were used to determine diagnostic accuracy. Adherence to EULAR drug management recommendations and Quality Indicators for treatment were compared between groups. RESULTS: In total, 138 patients were studied. The mean (SD) age was 71.3 (13.4) years and 70% were men. Forty-eight (35%) patients had gout on admission, 90 (65%) during their hospital stay, and 8 (6%) had multiple attacks. A total of 79 (57%) patients had a rheumatology consultation. These patients had more joints involved (p < 0.001), more frequent synovial fluid analysis (p < 0.001), and fulfilled ACR classification criteria more frequently than those who did not have a rheumatology consultation (65% vs 37%; p = 0.002). Intraarticular corticosteroid use was more common (44% vs 12%; p < 0.001) in patients who were seen by rheumatology. In contrast, colchicine was used more frequently in controls (63% vs 40%; p = 0.006). Patients seen by rheumatology were more likely to use nonsteroidal antiinflammatory drugs or colchicine for gout prophylaxis while titrating allopurinol to target (p = 0.033). CONCLUSION: A rheumatology consultation service for hospitalized patients with gout significantly improved the diagnostic accuracy and adherence to established guidelines for short and longterm treatment.
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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 | 0.002 |
| 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".