Development of a web-based decision aid for initiating biological or targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) in axial spondyloarthritis
Bibliographic record
Abstract
OBJECTIVES: To develop a web-based evidence-based decision aid to support shared decision-making in patients with axial spondyloarthritis (axSpA) who face a treatment decision to initiate or switch a biological or targeted synthetic disease modifying antirheumatic drug (b/tsDMARDs). METHODS: Through an iterative process, we systematically developed a decision aid based on evidence from the literature, explorative needs assessment interviews among patients and care providers, and input from experts of the SpA working group of the Dutch Society for Rheumatology and professionals on patient information employed at the Dutch Arthritis Society. The usability, ease of use and feasibility of the pilot version were tested among stakeholders and feedback was used to adapt the decision aid. Finally, a multifaceted strategy was used to introduce the decision aid in clinical practice. RESULTS: The decision aid consists of (1) consultation support instructions in the context of disease control and treatment needs, (2) an overview of available treatment options for axSpA, (3) detailed information on b/tsDMARDs and an interactive option grid that facilitates comparison of characteristics and (4) a final check supporting patients to deliberate on the decision to initiate or switch a b/tsDMARD. Rheumatologists introduced the decision aid in several Dutch rheumatology settings and the Dutch Arthritis Society posted it on their website, social media and in their monthly newsletter. CONCLUSION: We developed an evidence-based decision aid to support axSpA patients who face a treatment decision to initiate or switch a b/tsDMARD and introduced this in clinical practice.
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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 | 0.025 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".