Magnetic Resonance Imaging in Rheumatoid Arthritis Clinical Trials: Emerging Patterns Based on Recent Experience
Bibliographic record
Abstract
OBJECTIVE: The current validated magnetic resonance imaging (MRI) scoring method for rheumatoid arthritis (RA) in clinical trials, RA MRI Score (RAMRIS), incorporates all metacarpophalangeal (MCP) and wrist joints except MCP-1. The experience with radiographic scoring, however, was that excluding certain bones in the wrist improved the discriminative power for changes over time. In this study, we pool MRI data from randomized controlled clinical trails (RCT) to determine which combination of MCP and wrist joints are most sensitive and discriminative for structural changes over time. METHODS: MR images from 4 multicenter RCT, including 522 RA patients, were read by 2 radiologists, using the RAMRIS scoring system for erosion, osteitis, and synovitis. In one RCT, joint-space narrowing (JSN) was assessed cross-sectionally by one radiologist using a previously validated method. Baseline frequencies of erosion, JSN, osteitis, and synovitis of different bones and joints in the hand and wrist were compared. Intraclass correlation coefficients between readers were determined for each location. Finally, 7 different combinations of bone/joint locations were compared for their ability to discriminate subjects showing increases or decreases from baseline greater than or equal to smallest detectable changes (SDC) at Weeks 12 or 24. RESULTS: Frequency of involvement and reliability for assessing change varied by location. As in earlier analyses, excluding certain wrist bones increased the percentage of subjects showing changes greater than or equal to SDC. CONCLUSION: These findings suggest that excluding wrist bones that do not frequently or reliably demonstrate structural changes improves the discriminative power of the RAMRIS scoring system.
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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 | 0.233 | 0.388 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".