Magnetic Resonance Imaging Bone Edema Is Not a Major Feature of Gout Unless There Is Concomitant Osteomyelitis: 10-year Findings from a High-prevalence Population
Bibliographic record
Abstract
OBJECTIVE: Magnetic resonance imaging (MRI) is commonly used in autoimmune inflammatory arthritis to define disease activity and damage, but its role in gout remains unclear. The aim of our study was to identify and describe the MRI features of gout. METHODS: Over a 10-year period we identified patients with gout who underwent MRI scanning of the hands or feet. Scans were reviewed for erosions, synovitis, tenosynovitis, tendinosis, bone edema, and tophi by a musculoskeletal radiologist and 2 rheumatologists in a blinded manner. MRI features in patients with uncomplicated gout were compared with features where concomitant osteomyelitis was diagnosed. RESULTS: A total of 47 patients with gout (51 scans) were included: 33 (70%) had uncomplicated gout and 14 (30%) had gout complicated by osteomyelitis. MRI features included tophi in 36 scans (71%), erosions in 35 (69%), bone edema in 27 (53%), synovitis in 15 (29%), tenosynovitis in 8 (16%), and tendinosis in 2 (4%). Uncomplicated gout and gout plus osteomyelitis did not differ for most MRI features. However, "severe bone marrow edema" was much more common in gout plus osteomyelitis, occurring in 14/15 scans (93%) compared with 3/36 scans (8%) in uncomplicated gout (OR 154.0, 95% CI 14.7-1612, p < 0.0001). Sensitivity and specificity of "severe bone edema" for concomitant osteomyelitis were 0.93 (95% CI 0.68-0.99) and 0.92 (95% CI 0.78-0.98), respectively. CONCLUSION: MRI reveals that gout affects the joints, bones, and tendons. Bone edema in patients with chronic tophaceous gout is frequently mild and this contrasts with the "severe bone edema" observed in patients with concomitant osteomyelitis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".