Bone Marrow Edema Is the Most Specific Finding for Rheumatoid Arthritis (RA) on Noncontrast Magnetic Resonance Imaging of the Hands and Wrists: A Comparison of Patients with RA and Healthy Controls
Bibliographic record
Abstract
OBJECTIVE: To evaluate the sensitivity and specificity of magnetic resonance imaging (MRI) in detecting erosions, bone edema, and synovitis in the metacarpophalangeal and wrist joints for rheumatoid arthritis (RA). METHODS: MRI scans of bilateral hands and wrists of 40 healthy subjects and 40 RA patients were performed using 0.2 T extremity-MRI and read blindly using a modified RA MRI (RAMRIS) system (no contrast injection, imaging in 1 plane only). To determine interreader reliability, images of 10 randomly selected subjects were read independently by a musculoskeletal radiologist. RESULTS: A total of 3360 bones were evaluated. Patients with RA had significantly more erosions as well as higher scores for bone edema and synovitis than healthy subjects. Age had a significant effect on the number of erosions in both groups. However, when disease duration was factored in, age became insignificant in RA patients. Erosion number correlated with positive rheumatoid factor and higher C-reactive protein values. The intraclass correlation coefficient between the 2 readers was 0.76 for individual joints and 0.88 for total scores. When having a single erosion was used as a positive test for RA, the sensitivity of this test was 90%, but the specificity was only 35%. Presence of bone edema provided 65% sensitivity and 82.5% specificity. Eliminating the lunate from scoring for bone edema increased the specificity to 87.5% while decreasing the sensitivity to 62.5%. CONCLUSION: While MRI is a highly sensitive tool for identifying and tracking the progression of erosions, erosions detected by MRI with measures commonly used in a rheumatologist's office (no contrast, imaging in 1 plane) provide low specificity for RA. Bone marrow edema is the most specific MRI lesion for RA in this setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".