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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".