Quantitative, small bore, 1 Tesla, magnetic resonance imaging of the hands of patients with rheumatoid arthritis.
Bibliographic record
Abstract
OBJECTIVE: To determine if quantitative hand images obtained from an office-based MRI extremity scanner reliably distinguish patients with rheumatoid arthritis from controls. METHODS: The hands of 39 patients suffering from rheumatoid arthritis were imaged using a small bore, 1.0 Tesla Magnetic Resonance Imager. Non-contrast images of the metacarpophalangeal joints and wrist joints were evaluated using a method based on the validated rheumatoid arthritis magnetic resonance imaging system (RAMRIS). The extent and degree of synovitis, bone edema and bone erosions was assessed. Derived scores were compared with the corresponding scores for groups of younger (n=14) and older (n=27) controls with no signs or symptoms of joint disease. RESULTS: The mean (+/-standard error) total joint scores were 0.3+/-0.2 for young controls, 11.5+/-2.4 for older controls and 34.1+/-6.0 for the patients with rheumatoid arthritis. The greatest difference between rheumatoid patients and older controls was observed for synovitis with scores that were greater by a factor of almost 6.5. Scores for erosions and edema were factors of 2.9 and 2.3 greater in rheumatoid arthritis than in controls. The relationship between scores for the same joints on the dominant and non-dominant sides was generally stronger than the relationship between the metacarpophalangeal and wrist joints of the same hand. CONCLUSION: These observations indicate that scoring of hand images obtained from a small bore, office based, 1.0 Tesla MR imager have clinical validity and may be used to distinguish patients with rheumatoid arthritis from aged matched controls.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".