Why Do Patients With Gout Not Take Allopurinol?
Bibliographic record
Abstract
OBJECTIVE: The objectives of this study were to examine the reasons patients give for nonadherence to allopurinol and to examine differences in intentional nonadherence for patients who did and did not achieve serum urate (SU) levels at treatment target. METHODS: Sixty-nine men with gout attending rheumatology clinics, all prescribed allopurinol for ≥ 6 months, completed the Intentional Non-Adherence Scale (INAS). Differences in the types of intentional nonadherence were analyzed between those who did and did not achieve SU at treatment target (< 0.36 mmol/L, 6 mg/dL). RESULTS: The most frequently endorsed reasons for not taking their urate-lowering therapies (ULT) were because participants wanted to lead a normal life (23%) or think of themselves as a healthy person again (20%). Patients also reported not taking allopurinol as a way of testing if they really needed it (22%). Participants with SU above target endorsed significantly more INAS items as reasons for not taking their medication, had more medicine-related concerns, and were more likely to give Testing treatment as a reason for nonadherence. Participants who were younger, single, and non-New Zealand European also endorsed more reasons for not taking their allopurinol. CONCLUSION: The major reasons behind the patient's decision not to take allopurinol relate to the desire to lead a normal life and the strategy of testing the treatment to see if they could reduce the dose without getting symptoms. These results provide some potentially modifiable targets for adherence interventions and some recommendations to clinicians about how to reframe ULT for patients in order to improve adherence.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".