Whole-body Magnetic Resonance Imaging in Inflammatory Arthritis: Systematic Literature Review and First Steps Toward Standardization and an OMERACT Scoring System
Bibliographic record
Abstract
OBJECTIVE: Whole-body magnetic resonance imaging (WB-MRI) is a relatively new technique that can enable assessment of the overall inflammatory status of people with arthritis, but standards for image acquisition, definitions of key pathologies, and a quantification system are required. Our aim was to perform a systematic literature review (SLR) and to develop consensus definitions of key pathologies, anatomical locations for assessment, a set of MRI sequences and imaging planes for the different body regions, and a preliminary scoring system for WB-MRI in inflammatory arthritis. METHODS: An SLR was initially performed, searching for WB-MRI studies in arthritis, osteoarthritis, spondyloarthritis, or enthesitis. These results were presented to a meeting of the MRI in Arthritis Working Group together with an MR image review. Following this, preliminary standards for WB-MRI in inflammatory arthritides were developed with further iteration at the Working Group meetings at the Outcome Measures in Rheumatology (OMERACT) 2016. RESULTS: The SLR identified 10 relevant original articles (7 cross-sectional and 3 longitudinal, mostly focusing on synovitis and/or enthesitis in spondyloarthritis, 4 with reproducibility data). The Working Group decided on inflammation in peripheral joints and entheses as primary focus areas, and then developed consensus MRI definitions for these pathologies, selected anatomical locations for assessment, agreed on a core set of MRI sequences and imaging planes for the different regions, and proposed a preliminary scoring system. It was decided to test and further develop the system by iterative multireader exercises. CONCLUSION: These first steps in developing an OMERACT WB-MRI scoring system for use in inflammatory arthritides offer a framework for further testing and refinement.
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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 | 0.083 | 0.151 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.041 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".