Adherence and Treat‐to‐Target Benchmarks in Older Adults With Gout Initiating Urate‐Lowering Therapy in Ontario, Canada: A Population‐Based Study
Bibliographic record
Abstract
OBJECTIVE: We sought to evaluate urate-lowering therapy (ULT) adherence and treatment-to-target (T2T) serum uric acid (SUA) levels among older adults with gout starting ULT. METHODS: We performed a population-based retrospective cohort study in Ontario, Canada in patients with gout aged ≥66 years newly dispensed ULT between 2010 and 2019. We defined successful T2T as patients having SUA levels <360 μmol/L (6 mg/dL) within 12 months after ULT dispensation. We also assessed adherence to ULT. Multilevel logistic regression clustered by ULT prescriber evaluated patient, physician, and prescription factors associated with reaching target SUA levels. RESULTS: Among 44,438 patients (mean ± SD age 76.0 ± 7.3 years; 64.4% male), 30,057 (67.6%) patients had ≥1 SUA test completed. Overall, 52.3% patients reached SUA target within 12 months, improving from 45.2% in 2010 to 61.2% in 2019 (P < 0.0001). ULT adherence was 55.3% overall and improved annually. Key factors associated with achieving T2T included febuxostat treatment (odds ratio [OR] 11.40, 95% confidence interval [95% CI] 5.10-25.43) (was only dispensed in 88 patients), ULT adherence (OR 5.17, 95% CI 4.89-5.47), allopurinol starting doses >50 mg (OR 2.53, 95% CI 2.14-2.99), colchicine/oral glucocorticoids co-prescription (OR 1.24, 95% CI 1.14-1.34), and ULT prescription from a rheumatologist. CONCLUSION: Only 52.3% of patients achieved an optimal SUA level within 1 year of ULT initiation. ULT adherence was suboptimal, although improving over time. ULT adherence and higher allopurinol starting doses had the strongest associations of achieving a target SUA level. This study highlights room for improvement in gout management and potential strategies to address care gaps.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".