Bone Marrow Edema on Magnetic Resonance Imaging (MRI) of the Sacroiliac Joints Is Associated with Development of Fatty Lesions on MRI over a 1-year Interval in Patients with Early Inflammatory Low Back Pain: A 2-year Followup Study
Bibliographic record
Abstract
OBJECTIVE: To assess whether bone marrow edema (BME) detected on magnetic resonance imaging (MRI) of the sacroiliac joints (MRI-SIJ) is associated with development of structural changes on both MRI and pelvic radiographs in patients with early inflammatory back pain (IBP). METHODS: Patients with IBP ≤ 2 years were followed for 2 years with annual MRI-SIJ. MRI were scored for BME and structural changes (erosions and fatty lesions). Pelvic radiographs were graded according to the modified New York (mNY) criteria. With generalized estimated equation analysis, a time trend in the structural change scores was investigated. RESULTS: Sixty-eight patients [38% male; mean (SD) age 34.9 (10.3) yrs] were included. During the 2-year followup, pelvic radiograph grading remained constant. On MRI, the number of erosions per patient increased significantly (mean score 2.5 at baseline and 3.5 at 2-yr followup; p = 0.05). A trend was found for an increase in the number of fatty lesions per patient (mean score 5.4 at baseline and 8.5 at 2-yr followup; p = 0.06). Overall, BME was associated with the development of fatty lesions (right SIJ: OR 3.13, 95% CI 1.06-9.20; left SIJ: OR 22.13, 95% CI 1.27-384.50), preferentially in quadrants showing resolution of BME. In contrast, BME (or the resolution thereof) was not associated with the development of erosions. CONCLUSION: BME at baseline, especially when it disappears over time, results in the development of fatty lesions, but an association with erosions could not be demonstrated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".