Profile of Indian Patients with Juvenile Onset Chronic Inflammatory Joint Disease Using the ILAR Classification Criteria for JIA: A Community-based Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To assess the current International League of Associations for Rheumatology (ILAR) classification criteria (Edmonton, 2001) for juvenile idiopathic arthritis (JIA) in Indian patients. METHODS: Out of 441 children, 330 with chronic joint pains were diagnosed with juvenile onset chronic inflammatory arthritis and followed in an observational cohort. Our study was carried out from 1994 to 2006 in a community rheumatology clinic. Emphasis was placed on obtaining data required by the ILAR system. Of the original group, 235 children were eventually classified as having JIA; 108 were examined during the first year of illness. RESULTS: We assigned 224 children (95%) to discrete JIA categories: enthesitis-related arthritis (ERA; 36%), oligoarthritis (OLA-persistent; 17%), polyarthritis rheumatoid factor (RF)-negative (17%), polyarthritis RF-positive (12%), systemic arthritis (8%), OLA-extended (4%), and psoriatic arthritis (1%). The remaining 11 children (5%) were classified with undifferentiated arthritis (mostly an overlap due to seropositive RF and/or HLA-B27). The prevalence of ERA (89% HLA-B27-positive) and seropositive RF was unexpectedly high. Although agreement (kappa > 0.79) with the American College of Rheumatology criteria and the European Spondylarthropathy Study Group criteria was good to excellent, the ILAR system was found to be more comprehensive and clinically homogeneous. However, some problems appear unique in our scenario. CONCLUSION: A wide-spectrum phenotype of JIA is demonstrated by an Indian cohort. Although useful, RF and HLA-B27 in this population proved problematic to the ILAR classification.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".