Development and Qualitative Evaluation of a Decision Support Tool for Withdrawal of Biologic Therapy in Nonsystemic Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
Introduction. Limited evidence guides pediatric rheumatologists on when to withdraw biologic therapy in children with juvenile idiopathic arthritis, resulting in wide variation in clinical practice. This study aimed to develop and evaluate a decision support tool (DST) based on expert opinion to support pediatric rheumatologists in making withdrawal decisions. Methods. A literature review, focus groups, interviews, and prior research informed the design of the prototype DST. Evaluation of the DST’s face validity, content validity, acceptance, and feasibility was conducted through user testing interviews and a survey among pediatric rheumatologists from the Netherlands and Canada. Findings were summarized using descriptive and qualitative content analyses. Results. The prototype DST requires input on relevant patient, disease, and treatment characteristics. Its primary output is the predicted likelihood of biologic therapy withdrawal. Pediatric rheumatologists can adjust the importance of characteristics and observe the resulting impact on withdrawal likelihood. Eleven pediatric rheumatologists participated in testing. Key themes identified included the need for 1) clear terminology to ensure consistent interpretation of model inputs, 2) concise instructions on how and when to adjust the relative importance of characteristics, and 3) practice rounds to build trust among pediatric rheumatologists in the DST’s output. Participants found the DST feasible for clinical use, with its main value in explaining decisions to patients and engaging them in the decision-making process. Suggested future improvements include tracking the outcomes of withdrawal decisions and integrating predictive models based on clinical data. Conclusions. The DST developed in this study was well-received. Its main value lies in helping pediatric rheumatologists explain their decisions to patients and parents. The top priority for further development is integrating scientific evidence on successful withdrawal decisions. Highlights Decision support tools that provide structure to decisions based on expert opinion can increase transparency and consistency in medical decision making in the absence of clinical evidence. Data from clinical vignette studies that use an experimental design to elicit treatment preferences can be used to predict treatment decision making. A decision support tool to support biologic therapy withdrawal decisions has the most value in explaining the decision to children with nonsystemic juvenile idiopathic arthritis and their parents.
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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 | 0.078 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".