Spondyloarthritis in a Pediatric Population: Risk Factors for Sacroiliitis
Bibliographic record
Abstract
OBJECTIVE: Pediatric rheumatologists may have an opportunity to diagnose sacroiliitis in its early stages, prior to the development of irreversible radiographic changes. Early diagnosis frequently requires magnetic resonance imaging (MRI), the use of which is limited by expense and requirement for sedation. We set out to identify features of juvenile spondyloarthritis (SpA) associated with the highest risk of sacroiliitis, to identify patients who may be candidates for routine MRI-based screening. METHODS: We reviewed the charts of 143 children seen at Texas Scottish Rite Hospital for Children diagnosed with SpA based on the International League of Associations for Rheumatology criteria for enthesitis-related arthritis or the Amor criteria for SpA. We performed logistic regression analysis to identify risk factors for sacroiliitis. RESULTS: A group of 143 children were diagnosed with SpA. Consistent with the diagnosis of SpA, 16% had psoriasis, 43% had enthesitis, 9.8% had acute anterior uveitis, and 70% were HLA-B27+. Fifty-three children had sacroiliitis, of which 11 cases were identified by imaging studies in the absence of suggestive symptoms or physical examination findings. Logistic regression analysis revealed that hip arthritis was a positive predictor of sacroiliitis, while dactylitis was a negative predictor. CONCLUSION: Children with SpA are at risk for sacroiliitis, which may be present in the absence of suggestive symptoms or physical examination findings. The major risk factor for sacroiliitis is hip arthritis, while dactylitis may be protective. Routine screening by MRI should be considered in patients at high risk of developing sacroiliitis.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".