Early Predictors of Juvenile Sacroiliitis in Enthesitis-related Arthritis
Bibliographic record
Abstract
OBJECTIVE: To identify early predictors of sacroiliac (SI) involvement in a cohort of patients with enthesitis-related arthritis (ERA). METHODS: During a 7-year followup period, all consecutive patients fulfilling the ILAR classification criteria for ERA were enrolled. Data collected included demographic, clinical and laboratory variables at disease onset, at the onset of inflammatory back pain, and at the last available followup visit. Pelvis radiographs and dynamic magnetic resonance imaging (MRI) scans for SI joints were obtained simultaneously in all patients who developed inflammatory back pain. RESULTS: Fifty-nine children with ERA were studied; 40 male, 19 female; median age at disease onset 9 years 4 months (range 6 yrs 6 mo - 13 yrs 3 mo). At a median interval after disease onset of 1 year 3 months, 21 children reported symptoms of inflammatory back pain. In all cases, radiographs of SI joints were negative, while dynamic MRI revealed acute sacroiliitis in 17 cases. Multivariate analysis showed that the early predictors of SI were the number of active joints (p < 0.03) and the number of active entheses (p < 0.001) at onset. CONCLUSION: In our cohort, roughly 30% of children with ERA/juvenile idiopathic arthritis develop clinical and MRI evidence of sacroiliitis, detectable with dynamic MRI as early as 1 year after disease onset. Additional data from larger case series are needed to assess the specificity and sensitivity of this technique in the early phase of the disease and to confirm the rate of SI involvement reported in this cohort.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".