The OMERACT-RAMRIS Rheumatoid Arthritis Magnetic Resonance Imaging Joint Space Narrowing Score: Intrareader and Interreader Reliability and Agreement with Computed Tomography and Conventional Radiography
Bibliographic record
Abstract
OBJECTIVE: To test the intrareader and interreader reliability of assessment of joint space narrowing (JSN) in rheumatoid arthritis (RA) wrist and metacarpophalangeal (MCP) joints on magnetic resonance imaging (MRI) and computed tomography (CT) using the newly proposed OMERACT-RAMRIS JSN scoring method, and to compare JSN assessment on MRI, CT, and radiography. METHODS: After calibration of readers, MRI and CT images of the wrist and second to fifth MCP joints from 14 patients with RA and 1 healthy control were assessed twice for JSN by 3 readers, blinded to clinical and imaging data. Radiographs were scored by the Sharp/van der Heijde method. Intraclass correlation coefficients (ICC) and smallest detectable differences (SDD) were calculated, and the performance of various simplified scores was investigated. RESULTS: Both MRI and CT showed high intrareader (ICC ≥ 0.95) and interreader (ICC ≥ 0.94) reliability for total (wrist + MCP) assessment of JSN. Agreement was generally lower for MCP joints than for wrist joints, particularly for CT. Intrareader SDD for MCP/wrist/MCP + wrist were 1.2/6.1/6.4 JSN units for MRI, while 2.7/8.3/9.9 JSN units for CT. JSN on MRI and CT correlated moderately well with corresponding radiographic JSN scores (MCP 2-5: 0.49 and 0.56; wrist areas assessed by Sharp/van der Heijde: 0.80 and 0.95), and high ICC between scores on MRI and CT were demonstrated (MCP: 0.94; wrist: 0.92; MCP + wrist: 0.92). CONCLUSION: The OMERACT-RAMRIS MRI JSN scoring system showed high intrareader and interreader reliability, and high correlation with CT scores of JSN. The suggested JSN score may, after further validation in longitudinal studies, become a useful tool in RA clinical trials.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".