Decision Aid–Led Tapering of Biologic and Targeted Synthetic Disease-Modifying Antirheumatic Drugs in Rheumatoid Arthritis: A Qualitative Study
Bibliographic record
Abstract
OBJECTIVE: To explore the experiences and perspectives of patients and rheumatologists on decision aid (DA)-led tapering of advanced therapy in rheumatoid arthritis (RA). METHODS: Semistructured interviews were completed with patients and rheumatologists, embedded within a pilot study of DA-led tapering (ie, dose reduction) of biologic disease-modifying antirheumatic drugs (bDMARDs) and targeted synthetic DMARDs (tsDMARDs) in RA. All patients were in sustained (≥ 6 mos) remission and had chosen to reduce their therapy after a DA-led shared decision with their rheumatologist. The rheumatologists included those participating in the pilot (n = 4), and those who were not (n = 8). Reflexive thematic analysis of audiotaped and transcribed interviews identified themes in the group experiences. RESULTS: Patients (n = 10, 6 female) unanimously found the DA easy to understand and felt confident in shared decision making about treatment tapering and managing flares. Rheumatologists' (n = 12, 5 female) perspectives on tapering bDMARDs and tsDMARDs varied widely, from very supportive to completely opposed, and influenced their views on the DA. Rheumatologists expressed concerns about patient comprehension, destabilizing a stable situation, risks of flare, and extending appointment times. Despite their initial reservations about sending the DA to all eligible patients ahead of appointments, 3 of 4 participating rheumatologists adopted this approach during the pilot, which had the benefit of facilitating patient-led conversations. CONCLUSION: A DA-led strategy for tapering advanced therapy in RA was acceptable to patients and feasible in practice. Sending patients a DA ahead of their appointment facilitated patient-led conversations about tapering.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".