Making Decisions About Stopping Medicines for Well‐Controlled Juvenile Idiopathic Arthritis: A Mixed‐Methods Study of Patients and Caregivers
Bibliographic record
Abstract
OBJECTIVE: Improved treatments for juvenile idiopathic arthritis (JIA) have increased remission rates. We conducted this study to investigate how patients and caregivers make decisions about stopping medications when JIA is inactive. METHODS: We performed a mixed-methods study of caregivers and patients affected by JIA, recruited through social media and flyers, and selected by purposive sampling. Participants discussed their experiences with JIA, medications, and decision-making through recorded telephone interviews. Of 44 interviewees, 20 were patients (50% ages <18 years), and 24 were caregivers (50% caring for children ages ≤10 years). We evaluated characteristics associated with high levels of reported concerns about JIA or medicines using Fisher's exact testing. RESULTS: Decisions about stopping medicines were informed by competing risks between disease activity and treatment. Participants who expressed more concerns about JIA were more likely to report disease-related complications (P = 0.002) and more motivated to continue treatment. However, participants expressing more concern about medicines were more likely to report treatment-related complications (P = 0.04) and felt more compelled to stop treatment. Additionally, participants considered how JIA or treatments facilitated or interfered with their sense of normalcy and safety, expressed feelings of guilt and regret about previous or potential adverse events, and reflected on uncertainty and unpredictability of future harms. Decision-making was also informed by trust in rheumatologists and other information sources (e.g., family and online support groups). CONCLUSION: When deciding whether to stop medicines whenever JIA is inactive, patients and caregivers weigh competing risks between disease activity and treatment. Based on our results, we suggest specific approaches for clinicians to perform shared decision-making regarding stopping medicines for JIA.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".