Magnetic Resonance Imaging Bone Edema at Enrollment Predicts Rapid Radiographic Progression in Patients with Early RA: Results from the Nagasaki University Early Arthritis Cohort
Bibliographic record
Abstract
OBJECTIVE: To clarify whether magnetic resonance imaging (MRI) bone edema predicts the development of rapid radiographic progression (RRP) in the Nagasaki University Early Arthritis Cohort of patients with early-stage rheumatoid arthritis (RA). METHODS: Patients with early-stage RA (n = 76) were enrolled and underwent 1.5-T MRI of both wrists and finger joints. Synovitis, bone edema, and bone erosion were evaluated using the Rheumatoid Arthritis Magnetic Resonance Imaging Scoring (RAMRIS). RRP was defined as an annual increment > 3 at 1 year by the Genant-modified Sharp score of plain radiographs. A multivariate logistic regression analysis was performed to establish the risk factors for RRP. RESULTS: Median disease duration at enrollment was 3 months. RRP was found in 12 of the 76 patients at 1 year. A univariate analysis revealed that matrix metalloprotease-3, RAMRIS bone edema score, and RAMRIS bone erosion score were associated with RRP. Multivariate logistic regression analyses demonstrated that the RAMRIS bone edema score at enrollment (5-point increase, OR 2.18, 95% CI 1.32-3.59, p = 0.002) was the only independent predictor of the development of RRP at 1 year. A receiver-operating characteristic analysis identified the best cutoff value for RAMRIS bone edema score as 5. RRP was significantly rare among the patients with a RAMRIS bone edema score < 5 at enrollment (2 from 50 patients). CONCLUSION: Our findings suggest that MRI bone edema is closely associated with the development of RRP in patients with early-stage RA. Physicians should carefully control the disease activity when MRI bone edema is observed in patients with early 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".