Clinical, Laboratory and Radiological Predictors of Extension of Oligoarticular Juvenile Idiopathic Arthritis: A Prospective Study
Bibliographic record
Abstract
Background: Juvenile rheumatoid arthritis (JRA) is the most common chronic rheumatic illness in children and is a significant cause of both short- and long-term disabilities. The aim of this study is to detect clinical, laboratory and radiological predictors of oligoarticular juvenile idiopathic arthritis (JIA), which could be used to identify children whose disease is likely to extend to a more severe phenotype. Methods : This study included 40 oligoarticular JIA patients with no more than two years disease duration. Patients were divided into 2 groups after 6 months of illness into persistent and extended phenotypes. All patients were subjected to clinical, laboratory and conventional radiological assessment. Results : In extended JIA patients there was a significant increase in age of patients, upper limb joint involvement, bilateral symmetrical arthritis, disease activity and functional outcome measures. Moreover, a significant elevation of ESR and CRP levels and RF positivity, as well as ANA negativity and radiological findings of joint inflammation were evident in extended phenotype. Conclusion : Many clinical, laboratory and radiological predictors of conversion to extended oligoarticular JIA patients were close to charactertics of polyarticular form of the disease. doi:10.4021/ijcp29w
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".