Childhood-Onset Sacroiliitis
Bibliographic record
Abstract
OBJECTIVE: The aims of this study were to describe disease associations of magnetic resonance imaging (MRI)-confirmed and clinically symptomatic sacroiliitis in pediatric patients with rheumatic diseases and to examine the relationship between patient characteristics and MRI findings of the sacroiliac joint (SIJ). METHODS: Demographic and clinical data were extracted from the electronic medical records of the patients with sacroiliitis followed in the last 5 years. Active inflammatory and structural damage lesions of the SIJ-MRI were examined by the modified Spondyloarthritis Research Consortium of Canada scoring system, and correlation analysis of these results with clinical characteristics was evaluated. RESULTS: A total of 46 symptomatic patients were found to have MRI-proven sacroiliitis of 3 different etiologies: juvenile idiopathic arthritis (JIA) (n = 17), familial Mediterranean fever (FMF) (n = 14), and chronic nonbacterial osteomyelitis (CNO) (n = 8). Seven patients, FMF and JIA (n = 6) and FMF and CNO (n = 1), had a co-diagnosis that might cause sacroiliitis. Although inflammation scores and structural damage lesions did not statistically differ between the groups, capsulitis and enthesitis on the MRI were more frequently detected in the CNO group. There was a negative correlation between symptom onset and inflammation scores of bone marrow edema. Disease composite scores and acute phase reactants were correlated with MRI inflammation scores. CONCLUSIONS: We demonstrated that JIA, FMF, and CNO were the major rheumatic causes of sacroiliitis in children originating from the Mediterranean region. Quantitative MRI scoring tools can be used to assess the inflammation and damage of the SIJ in rheumatic diseases, show discrepancies between them, and have an important correlation with various clinical and laboratory features.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".