Fat Metaplasia in Inflammatory Sacroiliitis and in Nonrheumatic Conditions: A Step Toward Better Characterization
Bibliographic record
Abstract
Spondyloarthritis (SpA), a group of inflammatory diseases of which ankylosing spondylitis is the prototype, typically presents with inflammation of the sacroiliac joints (SIJ), or sacroiliitis1,2. The article by Ziegeler, et al in this issue of The Journal 3 describes the prevalence of periarticular sacroiliitis-like structural magnetic resonance imaging (MRI) changes of a patient population with low back pain and clinically suspected sacroiliitis. The authors’ main results provide important new insights into the MRI distribution pattern of periarticular fat metaplasia of the SIJ across different age groups in nonrheumatic subjects and numerous specific pathologic conditions. To date, MRI is considered the most sensitive and specific imaging modality for diagnosing and evaluating SIJ inflammation in patients with early disease4. The introduction of biological drugs such as the tumor necrosis factor-α receptor blocker group and their beneficial effect on SpA resulted in an accelerated use of MRI for early detection of sacroiliitis4,5. As a result, an increasing number of SIJ MRI are performed each year on patients with suspected sacroiliitis6. The main acute inflammatory finding detected on MRI, but not on radiographs, is periarticular bone marrow edema, or osteitis. Structural changes such as erosions, sclerosis, and ankylosis can be seen on pelvic radiographs and computed tomography in advanced, already established disease. However, like osteitis, periarticular fatty replacement, or fat metaplasia, is not seen on radiographs and can be reliably detected only on MRI. The Assessment of SpondyloArthritis international Society (ASAS) classification system for axial SpA (axSpA) is based on whether patients meet clinical or imaging criteria5, … Address correspondence to Prof. I. Eshed, Department of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer 52621, Israel. E-mail: iriseshed{at}gmail.com
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".