Rheumatoid Arthritis Magnetic Resonance Imaging Score Predicts Therapy Response: Results of the German ArthroMark Cohort
Bibliographic record
Abstract
OBJECTIVE: Markers for treatment response in rheumatoid arthritis (RA) are lacking. The aim of the study was to assess the performance of the RA magnetic resonance imaging (MRI) scoring system (RAMRIS) in combination with serum biomarkers to predict response to methotrexate (MTX) treatment in therapy-naive patients with early RA by using high-field MRI. METHODS: Twenty-eight patients with RA were prospectively assessed with baseline 3-T MRI of the clinical dominant hand, 3 and 6 months after MTX. The patients met the 2010 American College of Rheumatology/European League Against Rheumatism (EULAR) criteria [average age 56.8 yrs (range 39-74); positive for rheumatoid factor and/or anticyclic citrullinated peptide antibodies; disease duration < 6 mos (range 2-23 weeks)]. RAMRIS and serum biomarkers consisting of various experimental proteins including receptor activator of nuclear factor-κB ligand (RANKL) were obtained. Remission or treatment response was defined according to EULAR. To adjust for intrapersonal correlation, generalized linear mixed models were used. RESULTS: Treatment response at 3 months was associated to low RAMRIS erosion subscores and low total RAMRIS scores (p = 0.019 and 0.03, respectively). Remission at 6 months was associated to low RANKL levels (p = 0.033). In multivariate analyses, response at 3 and 6 months was predicted more accurately with the inclusion of total RAMRIS score, RAMRIS synovitis subscore at the second metacarpophalangeal (MCP) joint, or a combination of the two (p value likelihood ratio test = 0.035, 0.035, and 0.041, respectively). Remission was more accurately predicted with inclusion of RANKL, with no significant predictive effect of MRI. CONCLUSION: Baseline total RAMRIS can predict EULAR response. RAMRIS synovitis subscore at the second MCP joint and RANKL are associated with response and remission, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".