Are Rheumatoid Arthritis Patients Discernible from Other Early Arthritis Patients Using 1.5T Extremity Magnetic Resonance Imaging? A Large Cross-sectional Study
Bibliographic record
Abstract
OBJECTIVE: Magnetic resonance imaging (MRI) is increasingly used in rheumatoid arthritis (RA) research. A European League Against Rheumatism (EULAR) task force recently suggested that MRI can improve the certainty of RA diagnosis. Because this recommendation may reflect a tendency to use MRI in daily practice, thorough studies on the value of MRI are required. Thus far no large studies have evaluated the accuracy of MRI to differentiate early RA from other patients with early arthritis. We performed a large cross-sectional study to determine whether patients who are clinically classified with RA differ in MRI features compared to patients with other diagnoses. METHODS: In our study, 179 patients presenting with early arthritis (median symptom duration 15.4 weeks) underwent 1.5T extremity MRI of unilateral wrist, metacarpophalangeal, and metatarsophalangeal joints according to our arthritis protocol, the foot without contrast. Images were scored according to OMERACT Rheumatoid Arthritis Magnetic Resonance Imaging Scoring (RAMRIS) by 2 independent readers. Tenosynovitis was also assessed. The main outcome was fulfilling the 1987 American College of Rheumatology (ACR) criteria for RA. Test characteristics and areas under the receiver-operator-characteristic curves (AUC) were evaluated. In subanalyses, the 2010 ACR/EULAR criteria were used as outcome, and analyses were stratified for anticitrullinated protein antibodies (ACPA). RESULTS: The ACR 1987 criteria were fulfilled in 43 patients (24.0%). Patients with RA had higher scores for synovitis, tenosynovitis, and bone marrow edema (BME) than patients without RA (p < 0.05). ACPA-positive patients had more BME (median scores 6.5 vs. 4.25, p = 0.016) than ACPA-negative patients. For all MRI features, the predictive value for the presence of RA was low (< 50%). For all MRI features the AUC were < 0.70. Patients who fulfilled ACR/EULAR 2010 criteria but not ACR87 criteria for RA had less synovitis than patients who were positive for RA according to both sets of criteria (p = 0.029). CONCLUSION: Although patients with RA had higher scores of MRI inflammation and ACPA-positive patients had more BME, the severity of MRI inflammation assessed according to RAMRIS does not accurately differentiate patients with RA from other early arthritis patients.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".