Patient Preferences for Disease-modifying Antirheumatic Drug Treatment in Rheumatoid Arthritis: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To summarize patients' preferences for disease-modifying antirheumatic drug (DMARD) therapy in rheumatoid arthritis (RA). METHODS: We conducted a systematic review to identify English-language studies of adult patients with RA that measured patients' preferences for DMARD or health states and treatment outcomes relevant to DMARD decisions. Study quality was assessed using a published quality assessment tool. Data on the importance of treatment attributes and associations with patient characteristics were summarized across studies. RESULTS: From 7951 abstracts, we included 36 studies from a variety of countries. Most studies were in patients with established RA and were rated as medium- (n = 19) or high-quality (n = 12). The methods to elicit preferences varied, with the most common being discrete choice experiment (DCE; n = 13). Despite the heterogeneity of attributes in DCE studies, treatment benefits (disease improvement) were usually more important than both non-serious (6 of 8 studies) and serious adverse events (5 of 8), and route of administration (7 of 9). Among the non-DCE studies, some found that patients placed high importance on treatment benefits, while others (in patients with established RA) found that patients were quite risk averse. Subcutaneous therapy was often but not always preferred over intravenous therapy. Patient preferences were variable and commonly associated with the sociodemographic characteristics. CONCLUSION: Overall, the results showed that many patients place a high value on treatment benefits over other treatment attributes, including serious or minor side effects, cost, or route of administration. The variability in patient preferences highlights the need to individualize treatment choices in RA.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".