Toward New Classification Criteria for Juvenile Idiopathic Arthritis: First Steps, Pediatric Rheumatology International Trials Organization International Consensus
Bibliographic record
Abstract
OBJECTIVE: To revise the current juvenile idiopathic arthritis (JIA) International League of Associations for Rheumatology (ILAR) classification criteria with an evidence-based approach, using clinical and routine laboratory measures available worldwide, to identify homogeneous clinical groups and to distinguish those forms of chronic arthritis typically seen only in children from the childhood counterpart of adult diseases. METHODS: The overall project consists of 4 steps. This work represents Step 1, a Delphi Web-based consensus and Step 2, an international nominal group technique (NGT) consensus conference for the new provisional Pediatric Rheumatology International Trials Organization JIA classification criteria. A future large data collection of at least 1000 new-onset JIA patients (Step 3) followed by analysis and NGT consensus (Step 4) will provide data for the evidence-based validation of the JIA classification criteria. RESULTS: In Step 1, three Delphi rounds of interactions were implemented to revise the 7 ILAR JIA categories. In Step 2, forty-seven questions with electronic voting were implemented to derive the new proposed criteria. Four disorders were proposed: (a) systemic JIA; (b) rheumatoid factor-positive JIA; (c) enthesitis/spondylitis-related JIA; and (d) early-onset antinuclear antibody-positive JIA. The other forms were gathered under the term "others." These will be analyzed during the prospective data collection using a list of descriptors to see whether the clustering of some of them could identify homogeneous entities. CONCLUSION: . These preliminary criteria will be formally validated with a dedicated project.
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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 | 0.155 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".