Clinical Features and Outcome Measures Across Still Disease (Systemic Juvenile Idiopathic Arthritis and Adult-Onset Still Disease) Cohorts Worldwide: A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: Multinational research is essential to improve recognition and management of systemic juvenile idiopathic arthritis (sJIA). Current cohorts vary in the clinical variables and outcome measures collected. Adult-onset Still disease (AOSD) and sJIA are widely considered to comprise a single disease spectrum; however, classification criteria and clinical tools differ between groups. This systematic literature review aimed to identify clinical features and outcome measures collected across sJIA and AOSD cohorts worldwide to guide the development of a minimal dataset for Still disease. METHODS: A literature search was conducted from 2000 to 2024 using Ovid MEDLINE, Embase, and Wiley Cochrane Library (Trials). Included articles were in English and described sJIA or AOSD cohorts of ≥ 20 patients, reporting patient characteristics, clinical and laboratory features, and outcome measures. RESULTS: A total of 240 articles were included (95 sJIA, 134 AOSD, 11 mixed), from 37 countries, describing 23,136 patients. International League of Associations for Rheumatology classification was used in 77.9% of sJIA studies, whereas 98.5% of AOSD studies used Yamaguchi criteria. There was no clear consensus on the definition of macrophage activation syndrome. Race and ethnicity were only reported in 11.7% of articles. Cohorts evaluated aligned on the most commonly collected laboratory items for both AOSD and sJIA, with some agreement among clinical features, whereas disease outcome measures used to evaluate and follow disease trajectory were variable. CONCLUSION: Data reporting across sJIA and AOSD cohorts for clinical characteristics and outcome measures is widely heterogeneous. Consensus on the identification of a standardized minimal dataset for Still disease cohorts is needed to foster future collaboration and improve patient outcomes.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".