Development and Validation of the OMERACT Rheumatoid Arthritis Magnetic Resonance Tenosynovitis Scoring System in a Multireader Exercise
Bibliographic record
Abstract
OBJECTIVE: To develop and validate a magnetic resonance imaging (MRI) tenosynovitis (TS) score for tendons at the wrist and metacarpophalangeal (MCP) joint levels in patients with rheumatoid arthritis (RA). METHODS: Axial T1-weighted precontrast and postcontrast fat-saturated MR image sets of the hands of 43 patients with RA initiating rituximab therapy were obtained at baseline and after 14, 26, 38, or 52 weeks. The MR images were scored twice by 4 readers. Nine tendon compartments of the wrist and 4 flexor tendon compartments at the MCP joints were assessed. Tenosynovitis was scored as follows: 0: No; 1: < 1.5 mm; 2: ≥ 1.5 mm but < 3 mm; 3: ≥ 3 mm peritendinous effusion and/or postcontrast enhancement. Intrareader and interreader intraclass correlation coefficients (ICC), smallest detectable change (SDC), percentage of exact and close agreement (PEA/PCA), and standardized response mean (SRM) were calculated. RESULTS: Intrareader and interreader ICC for status and change scores were very good (≥ 0.80) for total scores for all readers. Intrareader SDC was ≤ 3.0 and interreader SDC was < 2.0. The overall PEA/PCA intrareader and interreader agreements for change scores in all tendons were 73.8%/97.6% and 47.9%/85.0%, respectively. Average SRM was moderate for total scores and 60.5% of the patients had a tenosynovitis change score ≥ SDC. CONCLUSION: The TS score showed high intrareader and interreader agreement for wrist and finger tendons, with moderate responsiveness, and the majority of the patients showed a change above the SDC. This scoring system may be included as a component of the RAMRIS.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".