Quantifying Bone Marrow Edema in the Rheumatoid Cervical Spine Using Magnetic Resonance Imaging
Bibliographic record
Abstract
OBJECTIVE: To determine the reliability and feasibility of a new magnetic resonance imaging (MRI) score to quantify bone marrow edema (BME), synovitis, and erosions in the cervical spine of patients with rheumatoid arthritis (RA); and to investigate the correlations among neck pain, clinical markers of RA disease activity, and MRI features of disease activity in the cervical spine. METHODS: Thirty patients with RA (50% with neck pain) and a Disease Activity Score 28-joint count > 3.2 had an MRI scan of their cervical spine. STIR, VIBE, and T1-weighted postcontrast sequences were used to quantify BME. MRI scans were scored for total BME, synovitis, and erosions using a new scoring method developed by the authors and assessed for reliability and feasibility. Associations between neck pain and clinical markers of disease activity were investigated. RESULTS: BME was present in 14/30 patients; 9/14 (64%) had atlantoaxial BME, 10/14 (71%) had subaxial BME, and 5/14 (36%) had both. Interobserver reliability for total cervical BME score was moderate [intraclass correlation coefficient (ICC) = 0.51]. ICC improved to 0.67 if only the vertebral bodies and dens were considered. There was no correlation between neck pain or clinical measures of RA disease activity and the presence of any MRI features including BME, synovitis, or erosions. CONCLUSION: Current RA disease activity scores do not identify activity in the cervical spine. An MRI score that quantifies BME, synovitis, and erosions in the cervical spine may provide useful information regarding inflammation and damage. This could alert clinicians to the presence of significant pathology and influence management.
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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 | 0.001 | 0.005 |
| 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) | 0.001 | 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".