The OMERACT Rheumatoid Arthritis Magnetic Resonance Imaging (MRI) Scoring System: Updated Recommendations by the OMERACT MRI in Arthritis Working Group
Bibliographic record
Abstract
OBJECTIVE: The Outcome Measures in Rheumatology (OMERACT) Rheumatoid Arthritis (RA) Magnetic Resonance Imaging (MRI) scoring system (RAMRIS), evaluating bone erosion, bone marrow edema/osteitis, and synovitis, was introduced in 2002, and is now the standard method of objectively quantifying inflammation and damage by MRI in RA trials. The objective of this paper was to identify subsequent advances and based on them, to provide updated recommendations for the RAMRIS. METHODS: MRI studies relevant for RAMRIS and technical and scientific advances were analyzed by the OMERACT MRI in Arthritis Working Group, which used these data to provide updated considerations on image acquisition, RAMRIS definitions, and scoring systems for the original and new RA pathologies. Further, a research agenda was outlined. RESULTS: Since 2002, longitudinal studies and clinical trials have documented RAMRIS variables to have face, construct, and criterion validity; high reliability and sensitivity to change; and the ability to discriminate between therapies. This has enabled RAMRIS to demonstrate inhibition of structural damage progression with fewer patients and shorter followup times than has been possible with conventional radiography. Technical improvements, including higher field strengths and improved pulse sequences, allow higher image resolution and contrast-to-noise ratio. These have facilitated development and validation of scoring methods of new pathologies: joint space narrowing and tenosynovitis. These have high reproducibility and moderate sensitivity to change, and can be added to RAMRIS. Combined scores of inflammation or joint damage may increase sensitivity to change and discriminative power. However, this requires further research. CONCLUSION: Updated 2016 RAMRIS recommendations and a research agenda were developed.
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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 | 0.025 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".