Predictors of Hip Disease in the Systemic Arthritis Subtype of Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Hip involvement occurs in 20%-40% of all cases of juvenile idiopathic arthritis (JIA). Patients with systemic JIA (sJIA) are affected most frequently. The aim of our study was to investigate the predictors of clinical hip disease and radiographic hip damage in sJIA. METHODS: The medical records (1997-2007) of all children (n = 98) with sJIA were reviewed. Potential clinical and laboratory predictors were examined at presentation and at 3 and 6 months. To account for censored observations, we used survival analysis. RESULTS: During the study period, 59 children met our inclusion criteria. The mean age at diagnosis was 7.8 years. Thirty patients (51%) developed clinical hip disease, with 12 (20%) developing radiographic evidence of hip damage. The median time to develop clinical hip disease was 24 months. Using Kaplan-Meier estimates, 25% of patients develop radiographically evident hip damage within 43 months. At presentation, patients in whom clinical hip disease later developed had polyarthritis (hazard ratio 2.51, p = 0.01), elevated IgG (HR 1.12, p = 0.01) and IgM (HR 2.71, p = 0.02), and higher CHAQ scores (HR 1.65, p = 0.02). At 3 months after disease onset, patients in whom radiographic hip damage later developed had fever (HR 4.78, p = 0.02), polyarthritis (HR 4.63, p = 0.02), and higher CHAQ scores (HR 3.20, p = 0.005). At 6 months, polyarthritis was the strongest predictor of both clinical hip disease and radiographic hip damage. CONCLUSION: Half of patients with sJIA develop clinical hip disease a median time of 24 months from diagnosis. Early identification of predictors of hip disease and damage in patients with sJIA may suggest earlier, more aggressive interventions to prevent joint destruction.
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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) | 0.002 | 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".