Magnetic Resonance Imaging of the Rheumatic Foot According to the RAMRIS System Is Reliable
Bibliographic record
Abstract
OBJECTIVE: In rheumatology, magnetic resonance imaging (MRI) is predominantly applied in the assessment and outcome measurement of rheumatoid arthritis (RA) in hands and wrists, leading to the development of the RAMRIS (RA-MRI-Scoring) system. It was initiated by the Outcome Measures in Rheumatoid Arthritis Clinical Trials (OMERACT). The RAMRIS system has not been applied widely in the measurement of feet. We investigated the interreader and intrareader agreement of the RAMRIS scoring system in the assessment of feet in RA. METHODS: Twenty-nine patients with RA who had radiological damage and/or arthritis underwent MRI. Two experienced readers independently read both complete sets. One reader read 6 random sets after the initial session, in order to assess the intrareader agreement. For evaluation of the intrareader and interreader reliability, quadratic-weighted κ scores were calculated per joint and lesion. RESULTS: For the forefeet, interreader scores were excellent, ranging from 0.77 (bone edema) to 0.95 (bone erosion). Hindfoot interreader agreement scores were highest for erosion (0.90) and synovitis global score (0.88), but edema and synovial thickness agreement were also acceptable (0.83 and 0.86). Intrareader scores were on the whole slightly lower, but excellent. CONCLUSION: Reliability (interreader and intrareader agreement) in the assessment of the rheumatoid foot according to the RAMRIS method is excellent.
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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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".