The Rate of Adherence to Antiarthritis Medications and Associated Factors among Patients with Rheumatoid Arthritis: A Systematic Literature Review and Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: Reported adherence in rheumatoid arthritis (RA) varies widely (10.5-98.5%). Variability may result in part from different methods used to measure adherence. Our aims were to quantify adherence to antiarthritis medications for each method and to identify variability and associated factors. METHODS: The systematic literature review examined PubMed, the Cochrane central database, and article reference lists from 1970 to November 2014. Papers with medication adherence data (disease-modifying antirheumatic drugs, steroids, and nonsteroidal antiinflammatory drugs) in adult patients with RA or data on associated factors were included. Adherence rate was recorded for each method. Random-effect metaanalysis estimated adherence for different evaluation methods. RESULTS: Adherence rate was 66% (95% CI 0.58-0.75). There were no differences in adherence among different measurement methods (interview, questionnaires, etc.). Regression analysis showed that adherence decreases during followup. Among 100 possible factors potentially effecting adherence, 7 adherence-associated factors were found in at least 2 different studies. These were the use of infliximab compared with etanercept or methotrexate (MTX), use of MTX compared to sulfasalazine or to etanercept, belief in the necessity of the medications, older age, and white race. CONCLUSION: Overall adherence rate was 66%. We suggest that readers appraise adherence studies according to the medications evaluated, the validity of the method, and the scales and cutpoints.
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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 | 0.028 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.044 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".