Magnetic Resonance Imaging of Bilateral Hands Is More Optimal Than MRI of Unilateral Hands for Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To explore the advantages of magnetic resonance imaging (MRI) of bilateral hands in rheumatoid arthritis (RA). METHODS: Consecutive patients with active RA were recruited for clinical assessments, radiographs, and MRI of bilateral hands. Bilateral hands were scanned simultaneously on 3.0 T whole-body MRI system and were scored on synovitis, osteitis, and bone erosion according to the RA MRI scoring (RAMRIS) system. RESULTS: Among 120 patients included, wrist bones and metacarpophalangeal joint (MCPJ) 2 proximal showed bone erosion in early RA. The second to fifth metacarpal bases and the second to fourth MCPJ distal showed more bone erosion in mid-stage or late-stage RA. When MRI of dominant unilateral hand was analyzed, MRI synovitis and osteitis in 5% of wrists and 3 MRI features in 5-14% of MCPJ were misdiagnosed (McNemar test, all p < 0.05). There were 46% wrist synovitis, 29-52% MCPJ2-5 synovitis, 45% wrist osteitis, and 20%-34% MCPJ2-5 osteitis not detected by joint tenderness and/or swelling. When the clinically more severe hand was selected for MRI of unilateral hand according to physical examination, MRI synovitis in 5% of wrists and 3 MRI features in 7-15% of MCPJ were misdiagnosed (all p < 0.05). Scatter plots and linear regression analyses were used to illustrate RAMRIS between dominant or selected hand (Y values) and nondominant or nonselected hand (X values). All linear models were markedly different from a Y = X linear model, indicating the dominant or clinically more severe hand could not represent the contralateral hand to evaluate RAMRIS. CONCLUSION: MRI of bilateral hands is more optimal than MRI of the unilateral hand in RA.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".