Progressive Increase in Sacroiliac Joint and Spinal Lesions Detected on Magnetic Resonance Imaging in Healthy Individuals in Relation to Age
Bibliographic record
Abstract
OBJECTIVE: Magnetic resonance imaging (MRI) plays a pivotal role in spondyloarthritis (SpA) diagnosis. However, a detailed description of MRI findings of the sacroiliac (SI) joints and spine in healthy individuals is currently lacking. This study was undertaken to evaluate the occurrence of MRI-detected SI joint and spinal lesions in healthy individuals in relation to age. METHODS: Ninety-five healthy subjects (ages 20-49 years) underwent MRI of the SI joints and spine. Bone marrow edema (BME) and structural lesions of the SI joints were scored using the Spondyloarthritis Research Consortium of Canada (SPARCC) method. Spinal inflammatory and structural lesions were evaluated using the SPARCC MRI spine inflammation index and the Canada-Denmark MRI scoring system, respectively. Fulfillment of the Assessment of SpondyloArthritis international Society definition of a positive MRI for sacroiliitis/spondylitis was reviewed. Findings were compared to MRIs of axial SpA patients from the Belgian Inflammatory Arthritis and Spondylitis cohort. RESULTS: Of the subjects ≥30 years old, 17.2% fulfilled the definition of a positive MRI for sacroiliitis, but this occurred rarely in younger subjects. SI joint erosions (20.0%) and fat metaplasia (13.7%) were detected across all age groups. Erosions were more frequently visualized in subjects ages ≥40 years (39.3%). Spinal BME (35.7%) and fat metaplasia (28.6%) were common in subjects older than 40 years. Nonetheless, only 1 subject had ≥3 corner inflammatory lesions. SI joint and spinal SPARCC scores and total structural lesions scores increased progressively with age. CONCLUSION: Contrary to what is commonly believed, structural MRI-detected SI joint lesions are frequently seen in healthy individuals. Especially in older subjects, the high occurrence of inflammatory and structural MRI-detected lesions impacts their specificity for SpA, which has important implications for the interpretation of MRIs in patients with a clinical suspicion of SpA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".