Structural Changes over a Short Period Are Associated with Functional Assessments in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To investigate the correlation between changes in radiological quantitative assessment with changes in clinical and functional assessment from baseline to 3 months in patients with rheumatoid arthritis (RA). METHODS: Twenty-eight patients with RA [methotrexate (MTX) and anti-tumor necrosis factor-α (TNF-α) group with high disease activity (n = 18); and MTX group with low disease activity (n = 10)] underwent assessments at baseline and 3 months: clinical [28-joint count Disease Activity Score (DAS28)], functional [Health Assessment Questionnaire (HAQ) and Michigan Hand Outcome Questionnaire (MHQ)], and imaging-based [3 Tesla magnetic resonance imaging (MRI) and high-resolution peripheral quantitative computed tomography (HR-pQCT)]. MR images were evaluated semiquantitatively [RA MRI scoring (RAMRIS)] and quantitatively for the volume of synovitis and bone marrow edema (BME) lesions. Erosion volumes were measured using HR-pQCT. RESULTS: After 3 months, the anti-TNF-α group demonstrated an improvement in disease activity through DAS28, HAQ, and MHQ. MRI showed significant decreases in synovitis and BME volume for the anti-TNF-α group, and significant increases in the MTX group. HR-pQCT showed significant decreases in bone erosion volume for the anti-TNF-α group, and significant increases in the MTX group. No significance was observed using RAMRIS. Changes in synovitis, BME, and erosion volumes, but not RAMRIS, were significantly correlated with changes in DAS28, HAQ, and MHQ. CONCLUSION: Quantitative measures were more sensitive than semiquantitative grading when evaluating structural and inflammatory changes with treatment, and were associated with patient clinical and functional outcomes. Multimodality imaging with 3T MRI and HR-pQCT may provide promising biomarkers that help determine disease progression and therapy response.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".