Adherence and Persistence with Methotrexate in Rheumatoid Arthritis: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Medication adherence in patients with rheumatoid arthritis (RA) is typically suboptimal. Nonadherence has been associated with symptom worsening and increased disability. We systematically reviewed published clinical studies to evaluate methotrexate (MTX) adherence and persistence, factors associated with MTX adherence and persistence, and the effect of MTX nonadherence on clinical outcomes in RA. METHODS: MEDLINE and Embase were systematically searched (inception to February 2016) using relevant keywords. Observational or interventional clinical studies in patients with RA that specifically reported adherence to or persistence with MTX were included. Data were extracted using a predesigned, standardized template that included study design, patient demographics, and relevant outcomes. Main outcomes were MTX adherence and persistence rates in patients with RA treated with MTX and factors associated with MTX adherence and persistence. RESULTS: Of 365 references screened, 31 articles met inclusion criteria and another 10 were identified from searching reference lists. Estimates of MTX adherence varied from study to study because of heterogeneity in patient populations, duration of followup, definitions of adherence, and methods of assessment. Rates of MTX persistence ranged from 50% to 94% at 1 year and 25% to 79% at 5 years. No clear trends were identified in factors that influence MTX adherence and persistence. Two studies suggested that MTX adherence was associated with superior clinical outcomes. CONCLUSION: MTX adherence and persistence are highly variable in patients with RA. Research is necessary to determine the effect of nonadherence on health outcomes and to identify independent predictors of nonadherence to inform evidence-based interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".