Early predictors of longterm outcome in patients with juvenile rheumatoid arthritis: subset-specific correlations.
Bibliographic record
Abstract
OBJECTIVE: To determine early predictors of longterm outcome in juvenile rheumatoid arthritis (JRA) in a multicenter cohort. METHODS: Patients were selected if they were > or = 8 years of age; the onset of arthritis occurred > or = 5 years before study; and a diagnosis of JRA was made at a participating center. Outcome variables were scores on self-administered Childhood Health Assessment Questionnaires (CHAQ) and active disease duration. Possible explanatory variables assessed included characteristics present at onset, HLA alleles, in particular the rheumatoid arthritis associated shared epitope (RASE), and radiographic indicators of joint damage within 2 years of onset. Data for 393 patients were available. Multivariate analyses were performed for the total group and for each onset subtype. RESULTS: Male sex correlated with worse disability in systemic onset JRA but less disability in RF negative, and a shorter active disease duration in RF positive polyarticular onset JRA. Positive antinuclear antibody correlated with a longer active disease duration in patients with pauciarticular onset JRA. Younger age at onset predicted longer active disease duration in pauciarticular and RF negative polyarticular, and a shorter active disease duration in systemic onset JRA. Residence on a reserve, rather than native North American race, correlated with worse disability. The RASE correlated with less disability in systemic JRA; but no correlation with outcome was evident for patients with rheumatoid factor positive polyarticular JRA. CONCLUSION: Variables predictive of longterm outcome in JRA are specific for each onset subtype. The most important early predictors were age at onset and sex of the patient. Place of residence may have a greater effect on disability than race. RASE may associate with a more favorable outcome in systemic onset disease.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".