Data-driven definitions for active and structural MRI lesions in the sacroiliac joint in spondyloarthritis and their predictive utility
Bibliographic record
Abstract
OBJECTIVES: To determine quantitative SI joint MRI lesion cut-offs that optimally define a positive MRI for inflammatory and structural lesions typical of axial SpA (axSpA) and that predict clinical diagnosis. METHODS: The Assessment of SpondyloArthritis international Society (ASAS) MRI group assessed MRIs from the ASAS Classification Cohort in two reading exercises where (A) 169 cases and 7 central readers; (B) 107 cases and 8 central readers. We calculated sensitivity/specificity for the number of SI joint quadrants or slices with bone marrow oedema (BME), erosion, fat lesion, where a majority of central readers had high confidence there was a definite active or structural lesion. Cut-offs with ≥95% specificity were analysed for their predictive utility for follow-up rheumatologist diagnosis of axSpA by calculating positive/negative predictive values (PPVs/NPVs) and selecting cut-offs with PPV ≥ 95%. RESULTS: Active or structural lesions typical of axSpA on MRI had PPVs ≥ 95% for clinical diagnosis of axSpA. Cut-offs that best reflected a definite active lesion typical of axSpA were either ≥4 SI joint quadrants with BME at any location or at the same location in ≥3 consecutive slices. For definite structural lesion, the optimal cut-offs were any one of ≥3 SI joint quadrants with erosion or ≥5 with fat lesions, erosion at the same location for ≥2 consecutive slices, fat lesions at the same location for ≥3 consecutive slices, or presence of a deep (i.e. >1 cm depth) fat lesion. CONCLUSION: We propose cut-offs for definite active and structural lesions typical of axSpA that have high PPVs for a long-term clinical diagnosis of axSpA for application in disease classification and clinical research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".