Patients’ attitudes towards deprescribing disease modifying anti-rheumatic drugs in rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVES: People living with rheumatoid arthritis (RA) may be taking one or more medications that they no longer need. The objective of this study was to determine the attitudes and beliefs regarding Disease Modifying Anti-Rheumatic Drug (DMARD) use and RA patients' willingness to have their medications deprescribed. METHODS: This cross-sectional study included adults aged ≥ 18 years with a diagnosis of RA and were currently taking methotrexate and/or hydroxychloroquine. Participants completed a self-administered survey, adapted from the validated revised Patients' Attitudes Towards Deprescribing (rPATD) questionnaire, tailored to patients living with RA. Additional sociodemographic and clinical data were collected to explore factors associated with participants' willingness to deprescribe their DMARDs. RESULTS: A total of 87 participants were recruited with median age of 66 years (IQR 57.5-73), and 57 (65.5 %) were female. The majority of RA patients (84 %) agreed they would be willing to stop one of their RA medicines if their rheumatologist said it was possible. Participants expressed greater concerns with ceasing their DMARDs compared to their other medications. No factors were found to be significantly associated with willingness to deprescribe DMARDs. CONCLUSION: Whilst people with RA are satisfied with their current therapy, most would be willing to have one or more of their DMARDs deprescribed if their rheumatologist supports it. PRACTICE IMPLICATIONS: Clinicians should be encouraged to initiate deprescribing discussions, especially in stable rheumatoid arthritis, however, shared decision-making should involve identifying and addressing concerns patients have about deprescribing their DMARDs.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".