Magnetic Resonance Imaging Compared to Conventional Radiographs for Detection of Chronic Structural Changes in Sacroiliac Joints in Axial Spondyloarthritis
Bibliographic record
Abstract
OBJECTIVE: We investigated the performance of magnetic resonance imaging (MRI) compared to conventional radiographs for detection of chronic structural changes in the sacroiliac joints (SIJ) in patients with axial spondyloarthritis (SpA). METHODS: We included 112 patients with definite axial SpA (68 with ankylosing spondylitis and 44 with nonradiographic axial SpA), for whom radiographs and MRI scans of the SIJ performed at the same time were available. Radiographs and MRI of the SIJ were scored for subchondral sclerosis (score 0-2), erosions (score 0-3), and joint space changes (score 0-5) in each SIJ. Readers provided an overall impression of the extent of damage according to the scoring system of the modified New York criteria. RESULTS: In total, 224 SIJ from 112 patients were available for analysis. There was rather low agreement between MRI and radiographs concerning definite erosions of SIJ (κ = 0.11), moderate agreement for definite subchondral sclerosis (κ = 0.46) and definite joint space abnormalities (κ = 0.41), and almost perfect agreement for joint ankylosis (κ = 0.85). MRI demonstrated a good overall performance in detection of definite "chronic" sacroiliitis, with a sensitivity of 84% and a specificity of 61%. For sacroiliitis fulfilling the modified New York criteria, MRI had a sensitivity of 81% and a specificity of 64% using radiographs as the reference method. CONCLUSION: MRI demonstrated good overall performance for detection of chronic structural changes in the SIJ as compared to radiographs.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| 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".