Testing an OMERACT MRI Scoring System for Peripheral Psoriatic Arthritis in Cross-sectional and Longitudinal Settings
Bibliographic record
Abstract
OBJECTIVE: Magnetic resonance imaging (MRI) is increasingly used to measure articular inflammation and damage in patients with psoriatic arthritis (PsA). We evaluated the reliability of a new OMERACT PsA MRI scoring system, PsAMRIS, in PsA fingers. METHODS: In 2 separate studies, MRI scans were obtained from patients with clinical evidence of synovitis or dactylitis of the fingers. For the first cross-sectional study, images were obtained at one timepoint. For the second longitudinal study, images were obtained at 2 timepoints, 6 weeks apart. Scans were scored using PsAMRIS in an international multireader setting, for synovitis, tenosynovitis, periarticular inflammation, bone edema, bone erosions, and bone proliferation. RESULTS: Global status scores from both datasets revealed moderate to high reliability for scoring most features, although reliability was poor for periarticular inflammation in the cross-sectional study. Change scores that reflected inflammatory activity also exhibited moderate to good reliability in the longitudinal exercise, despite there being very little absolute change in MRI synovitis or tenosynovitis observed in this dataset. At the distal interphalangeal joints, reliability for change scores was acceptable only for synovitis and tenosynovitis. CONCLUSION: Further development and testing of the PsAMRIS is planned to improve its performance as a clinical and research tool to identify and measure pathology in peripheral joint PsA.
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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 | 0.022 | 0.042 |
| 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 | 0.001 |
| Open science | 0.001 | 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".