Adherence Rates and Associations with Nonadherence in Patients with Rheumatoid Arthritis Using Disease Modifying Antirheumatic Drugs
Bibliographic record
Abstract
OBJECTIVE: Nonadherence in patients with rheumatoid arthritis (RA) using disease modifying antirheumatic drugs (DMARD) may result in unnecessarily high levels of disease activity and function loss. The aim of this descriptive study was to assess adherence rates with self-report measures in a large random population, and to identify potential risk factors for nonadherence. METHODS: A randomly selected sample of 228 patients with RA using DMARD was invited for a standardised interview. For each medicine, the patients were asked about adherence, consumption and perceived (side) effects. After the interview, the patients received self-report questionnaires to assess adherence [Compliance Questionnaire on Rheumatology (CQR) and the Medication Adherence Scale (MARS)], coping, beliefs about medicines, satisfaction about medicine information, and physical functioning. Subsequently, associations between adherence and demographics, clinical characteristics, and patient attitudes were examined. RESULTS: Depending on the instrument used, 68% (CQR) and 60% (MARS) of the patients were adherent to DMARD. Nonadherence was not associated with demographic and clinical characteristics, satisfaction about information, medication concerns, and coping styles. The disease duration, the number of perceived side-effects, and beliefs about the necessity of the medicine were weakly associated with adherence. CONCLUSION: In this large study with a random RA population, 32%-40% of the patients did not adhere to their DMARD prescription. As none of the possible risk factors was strongly related to adherence, no general risk factor seems to be powerful enough as a possible screening tool or target for adherence-improving interventions. This implies that nonadherence barriers should be assessed on an individual basis.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".