Development and Preliminary Validation of a Magnetic Resonance Imaging Joint Space Narrowing Score for Use in Rheumatoid Arthritis: Potential Adjunct to the OMERACT RA MRI Scoring System
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a magnetic resonance imaging (MRI) method of assessment of joint space narrowing (JSN) in rheumatoid arthritis (RA). METHODS: Phase A: JSN was scored 0-4 on MR images of 5 RA patients and 3 controls at 15 wrist sites and 2nd-5th metacarpophalangeal (MCP) joints by 8 readers (7 once, one twice), using a preliminary scoring system. Phase B: Image review, discussion, and consensus on JSN definition, and revised scoring system. Phase C: MR images of 15 RA patients and 4 controls were scored using revised system by 5 readers (4 once, one twice), and results compared with radiographs [Sharp-van der Heijde (SvdH) method]. RESULTS: Phase A: Intraobserver agreement: intraclass correlation coefficient (ICC) = 0.99; smallest detectable difference (SDD, for mean of readings) = 2.8 JSN units (4.9% of observed maximal score). Interobserver agreement: ICC = 0.93; SDD = 6.4 JSN units (9.9%). Phase B: Agreement was reached on JSN definition (reduced joint space width compared to normal, as assessed in a slice perpendicular to the joint surface), and revised scoring system (0-4 at 17 wrist sites and 2nd-5th MCP; 0: none; 1: 1-33%; 2: 34-66%; 3: 67-99%; 4: ankylosis). Phase C: Intraobserver agreement: ICC = 0.90; SDD = 6.8 JSN units (11.0%). Interobserver agreement: ICC = 0.92 and SDD = 6.2 JSN units (8.7%). The correlation (ICC) with the SvdH radiographic JSN score of the wrist/hand was 0.77. Simplified approaches evaluating fewer joint spaces demonstrated similar reliability and correlation with radiographic scores. CONCLUSION: An MRI scoring system of JSN in RA wrist and MCP joints was developed and showed construct validity and good intra- and interreader agreements. The system may, after further validation in longitudinal data sets, be useful as an outcome measure in RA.
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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 | 0.014 | 0.020 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".