Validity and Responsiveness of Combined Inflammation and Combined Joint Damage Scores Based on the OMERACT Rheumatoid Arthritis MRI Scoring System (RAMRIS)
Bibliographic record
Abstract
OBJECTIVE: The RAMRIS [Outcome Measures in Rheumatology rheumatoid arthritis (RA) magnetic resonance imaging (MRI) Scoring system] is used in clinical RA trials. We have investigated methods to combine the RAMRIS features into valid and responsive scores for inflammation and joint damage. METHODS: We used data from 3 large randomized early RA trials to assess 5 methods to develop a combined score for inflammation based on RAMRIS bone marrow edema, synovitis, and tenosynovitis scores, and a combined joint damage score based on erosions and joint space narrowing. Methods included unweighted summation, normalized summation, and 3 different variants of weighted summation of the RAMRIS features. We used a derivation cohort to calculate summation weights to maximize the responsiveness of the combined score. Construct validity of the combined scores was examined by assessing correlations to imaging, clinical, and biochemical measures. Responsiveness was tested by calculating the standardized response mean (SRM) and the relative efficiency of each score in a validation cohort. RESULTS: Patient characteristics, as well as baseline and followup RAMRIS scores, were comparable between cohorts. All combined scores were significantly correlated to other imaging, clinical, and biochemical measures. Inflammation scores combined by normalized and weighted summation had significantly higher responsiveness in comparison to unweighted summation, with SRM (95% CI) for unweighted summation 0.62 (0.51-0.73), normalized summation 0.73 (0.63-0.83), and weighted summation 0.74 (0.64-0.84). For the damage score, there was a trend toward higher responsiveness for weighted summation. CONCLUSION: Combined MRI scores calculated by normalized or weighted summation of individual MRI pathologies were valid and responsive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.067 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".