A survey of national and multi-national registries and cohort studies in juvenile idiopathic arthritis: challenges and opportunities
Bibliographic record
Abstract
BACKGROUND: To characterize the existing national and multi-national registries and cohort studies in juvenile idiopathic arthritis (JIA) and identify differences as well as areas of potential future collaboration. METHODS: We surveyed investigators from North America, Europe, and Australia about existing JIA cohort studies and registries. We excluded cross-sectional studies. We captured information about study design, duration, location, inclusion criteria, data elements and collection methods. RESULTS: We received survey results from 18 studies, including 11 national and 7 multi-national studies representing 37 countries in total. Study designs included inception cohorts, prevalent disease cohorts, and new treatment cohorts (several of which contribute to pharmacosurveillance activities). Despite numerous differences, the data elements collected across the studies was quite similar, with most studies collecting at least 5 of the 6 American College of Rheumatology core set variables and the data needed to calculate the 3-variable clinical juvenile disease activity score. Most studies were collecting medication initiation and discontinuation dates and were attempting to capture serious adverse events. CONCLUSION: There is a wide-range of large, ongoing JIA registries and cohort studies around the world. Our survey results indicate significant potential for future collaborative work using data from different studies and both combined and comparative analyses.
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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 | 0.222 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".