Medication Adherence in Gout: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Recent data suggesting the growing problem of medication nonadherence in gout have called for the need to synthesize the burden, determinants, and impacts of the problem. Our objective was to conduct a systematic review of the literature examining medication adherence among patients with gout in real-world settings. METHODS: We conducted a search of Medline, Embase, International Pharmaceutical Abstracts, PsycINFO, and CINAHL databases and selected studies of gout patients and medication adherence in real-world settings. We extracted information on study design, sample size, length of followup, data source (e.g., prescription records versus electronic monitoring versus self-report), type of nonadherence problem evaluated, adherence measures and reported estimates, and determinants of adherence reported in multivariable analyses. RESULTS: We included 16 studies that we categorized according to methods used to measure adherence, including electronic prescription records (n = 10), clinical records (n = 1), electronic monitoring devices (n = 1), and self-report (n = 4). The burden of nonadherence was reported in all studies, and among studies based on electronic prescription records, adherence rates were all below 0.80 and the proportion of adherent patients ranged from 10-46%. Six studies reported on determinants, with older age and having comorbid hypertension consistently shown to be positively associated with better adherence. One study showed the impact of adherence on achieving a serum uric acid target. CONCLUSION: With less than half of gout patients in real-world settings adherent to their treatment, this systematic review highlights the importance of health care professionals discussing adherence to medications during encounters with patients.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".