One-Year Outcomes After Decision Aid-Led Tapering of Advanced Therapy in Rheumatoid Arthritis
Bibliographic record
Abstract
Objectives The Canadian Rheumatology Association clinical practice guidelines recommend offering tapering of advanced therapy to people with rheumatoid arthritis (RA) who are in sustained remission and provide a decision aid to support these discussions. Our goal was to understand the impact of implementing the decision aid on treatment choices and outcomes in RA patients in sustained remission. Methods We conducted a single-center pilot study with 4 rheumatologists in Calgary Alberta. Rheumatologists were initially asked to identify people in their practice at the time of the clinic visit. After identifying and addressing initial recruitment barriers, we switched to sending people a decision aid 1 month ahead of their appointment. While rheumatologists and patients were free to decide how fast to taper, and when to see their rheumatologist next, they were guided by CRA recommendations, which suggested decreasing the dose by ~25% at a time, with 3-month delays between subsequent reductions. Patients were followed for a year and the primary outcome (safety) was the proportion of patients who had to switch to another advanced therapy due to inefficacy. Results Thirty-four patients chose to taper their advanced therapy; 13 from in-clinic discussions, and 21/83 (25%) who received a decision aid ahead of their appointment. Of the 34 people, 32 consented to the follow-up study (mean age 55, 72% female). All patients were taking full dose advanced therapy at the time of recruitment. Patients reduced their TNF inhibitor (n=19), IL-6-blocker (n=5) JAK-inhibitor (n=4), and T-cell inhibitor (n=4). Among the 26 patients who have completed follow-up to date (last follow-up October 2024), 8 (31%) had a flare requiring re-escalation of their dose, and another 3 (12%) re-escalated their dose for other reasons (typically discomfort with being on a lower dose and the potential to have a flare). One patient switched to another agent without first re-escalating their dose. Of the 15 patients who maintained a dose reduction over 1 year, the mean dose reduction at 1 year was 47%. Of the 9 patients who re-escalated their dose after tapering and have completed the end of study survey, 8 (89%) agreed or strongly agreed with the statement “I am glad I tried reducing my medication.” The remaining patient was neutral. Conclusion Sending people a guideline-linked decision aid ahead of their appointment resulted in 25% of people choosing to reduce their treatment, which was safe for over 1 year, and without decisional regret in people who had to re-escalate their dose.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".