Quantification of Bone Marrow Edema by Magnetic Resonance Imaging Only Marginally Reflects Clinical Neck Pain Evaluation in Rheumatoid Arthritis and Ankylosing Spondylitis
Bibliographic record
Abstract
OBJECTIVE: Neck pain is common in rheumatoid arthritis (RA) and ankylosing spondylitis (AS). We investigated the correlation of bone marrow edema (BME) on magnetic resonance imaging (MRI) in RA and AS and its association with clinical complaints of neck pain. METHODS: Cervical spine short-tau inversion recovery-MRI and T1w-MRI of 34 patients with RA and 6 patients with AS complaining about neck pain were obtained. Clinical and laboratory data were available. BME was scored by 2 blinded readers using a modification of a published score, including various cervical sites. Degenerative changes were also quantified. RESULTS: Patients were predominantly women (82.5%), and mean ± SD age was 57.5 ± 11.8 years, C-reactive protein (CRP) was 0.8 ± 1.3 mg/dl, and pain score was 46.0 ± 17.5. BME was detected in 24/40 patients (60%) involving the atlantoaxial region (21%), vertebral bodies (75%), facet joints (29%), and spinous processes (46%). Degenerative changes were identified in 21/40 patients (52.5%), 13 (62%) of whom also had BME in vertebral bodies. No differences were found between patients with versus without cervical BME for clinical assessments: numeric rating scale pain (median ± interquartile range) 5.5 ± 3.0 vs 6.0 ± 4.0 (p = 0.69), Funktionsfragebogen Hannover 68.2 ± 41.0 vs 42.0 ± 55.5 (p = 0.19), Northwick pain score 44.4 ± 21.8 vs 47.2 ± 27.0 (p = 0.83), or CRP 0.40 ± 0.80 vs 0.60 ± 0.66 (p = 0.94). For patients with degenerative changes, symptom duration was longer than for patients without (10 ± 12.5 vs 5.0 ± 18.0 yrs, p = 0.73). CONCLUSION: In this small study of patients with RA and AS complaining about neck pain, BME was found in many different cervical sites, including the facet joints and the spinous processes. However, the occurrence and severity of BME did not correlate with the severity of neck pain.
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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 | 0.006 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".