Developing a disease activity tool for systemic-onset juvenile idiopathic arthritis by international consensus using the Delphi approach
Bibliographic record
Abstract
OBJECTIVES: The systemic form of juvenile idiopathic arthritis may present with many diverse symptoms, signs and laboratory abnormalities. Our aim was to elicit and pool items useful for developing a consensus disease activity measure for systemic arthritis in children, using an international pool of respondents. METHODS: We used a Delphi survey process in two steps. First we surveyed 187 paediatric rheumatologists and allied health professionals. We elicited 2607 items that, when combined with previously elicited items from parents/patients, could be pooled into 107 independent items. We then surveyed the paediatric rheumatologists to determine the frequency and importance of the 107 items. RESULTS: Our response rate was 83% to both surveys. We identified 29 items as being the most important and most frequently seen indicators of active disease. The most highly rated of these items were: presence of fever, presence of rash, elevated ESR, elevated CRP, requirement for increasing medications, abnormal physician global evaluation and presence of joints with active arthritis. CONCLUSIONS: Twenty-nine items are thought by medical practitioners to be most relevant in determining disease activity in systemic arthritis. As a next step, the measurement properties of these items will be tested to help develop a disease activity tool.
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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 | 0.127 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".