<scp>Data‐Driven</scp> Magnetic Resonance Imaging Definitions for Active and Structural Sacroiliac Joint Lesions in Juvenile Spondyloarthritis Typical of Axial Disease: A <scp>Cross‐Sectional</scp> International Study
Bibliographic record
Abstract
OBJECTIVE: We aimed to determine quantitative sacroiliac (SI) joint magnetic resonance imaging (MRI) cutoffs for active and structural lesions that will be incorporated as imaging domains in classification criteria of axial disease in juvenile spondyloarthritis (SpA). METHODS: MRI scans from an international cross-section of juvenile SpA patients were reviewed by 6 musculoskeletal imaging experts blinded to clinical details. Raters globally assessed the presence/absence of lesions typical of axial SpA and performed SI joint quadrant- or joint-based scoring. Sensitivity and specificity of lesion cutoffs were calculated using a rater majority (≥4 of 6 raters) on a global assessment of the presence/absence of active or structural lesions typical of axial SpA with high confidence as the reference standard. Cutoffs were validated in an independent cohort. RESULTS: Imaging from 243 subjects, 61% male, median age 14.9 years, had sequences available for detailed MRI scoring. Optimal cutoffs for defining lesions typical of axial disease in juvenile SpA were: 1) inflammatory lesion: bone marrow edema in ≥3 SI joint quadrants across all SI joint MRI slices (sensitivity 98.6%, specificity 96.5%); 2) structural lesions: erosion in ≥3 quadrants or sclerosis or fat lesion in ≥2 SI joint quadrants or backfill or ankylosis in ≥2 joint halves across all SI joint MRI slices (sensitivity 98.6%, specificity 95.5%). Sensitivity and specificity of the optimal cutoffs in the validation cohort were excellent. CONCLUSION: We propose data-driven cutoffs for active inflammatory and structural lesions on MRI typical of axial disease in juvenile SpA that have high specificity and sensitivity using central imaging global assessment as the reference standard and excellent reliability.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".