Serum Urate Monitoring Among Older Adults With Gout: Initiating <scp>Urate‐Lowering</scp> Therapy in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To assess the proportion of, and factors associated with, older adults with gout receiving a serum urate (SUA) test after starting urate-lowering therapy (ULT). METHODS: We performed a population-based retrospective cohort study in Ontario, Canada in patients ages ≥66 years with gout, newly dispensed ULT between 2010 and 2019. We characterized patients with SUA testing within 6 and 12 months after ULT dispensation. Multilevel logistic regression clustered by ULT prescriber evaluated the factors associated with SUA monitoring within 6 months. RESULTS: We included 44,438 patients with a mean ± SD age of 76.0 ± 7.3 years and 64.4% male. Family physicians prescribed 79.1% of all ULTs. SUA testing was lowest in 2010 (56.4% at 6 months) and rose over time to 71.3% in 2019 (P < 0.0001). Compared with rheumatologists, family physicians (odds ratio [OR] 0.26 [95% confidence interval (95% CI) 0.23-0.29]), internists (OR 0.34 [95% CI 0.29-0.39]), nephrologists (OR 0.37 [95% CI 0.30-0.45]), and other specialties (OR 0.25 [95% CI 0.21-0.29]) were less likely to test SUA, as were male physicians (OR 0.87 [95% CI 0.83-0.91]). Patient factors associated with lower odds of SUA monitoring included rural residence (OR 0.81 [95% CI 0.77-0.86]), lower socioeconomic status (OR 0.91 [95% CI 0.85-0.97]), and patient comorbidities. Chronic kidney disease, hypertension, diabetes mellitus, and coprescription of colchicine/oral corticosteroids (OR 1.31 [95% CI 1.23-1.40]) were correlated with increased SUA testing. CONCLUSION: SUA testing is suboptimal among older adults with gout initiating ULT but is improving over time. ULT prescriber, patient, and prescription characteristics were correlated with SUA testing.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".