Magnetic resonance imaging assessment of spinal inflammation in ankylosing spondylitis: Standard clinical protocols may omit inflammatory lesions in thoracic vertebrae
Bibliographic record
Abstract
OBJECTIVE: Radiologic assessment of spinal inflammation in patients with ankylosing spondylitis (AS) relies primarily on magnetic resonance imaging (MRI), although little is known about the distribution of inflammatory lesions within the structures of the spine. Our objective was to compare the distribution of inflammatory lesions centrally and laterally within the thoracic and lumbar spine vertebral bodies. METHODS: We studied 49 patients with AS who were scanned with STIR and T1-weighted spin-echo MRI of the whole spine. Scans were read by 2 musculoskeletal radiologists, with a third reader as the arbitrator. Controls included 6 age-matched individuals. We recorded bone marrow edema on STIR images from each vertebral body, separately identifying central and lateral slices. The latter were defined as images that included or were lateral to the pedicle. Interreader reproducibility was assessed by kappa statistics. RESULTS: Inflammation was present in 263 (45%) of 588 thoracic and 86 (35%) of 245 lumbar vertebrae; the mean number of affected thoracic and lumbar vertebrae per patient were 5.4 and 1.8, respectively. Inflammation was present in the lateral aspect of 219 (37%) of 588 thoracic vertebrae and 45 (18%) of 245 lumbar vertebrae (P < 0.001). Lesions were more common laterally than centrally for all thoracic vertebrae except for T7. Involvement of only the lateral slices was observed in as many as 19.6% of thoracic vertebrae. CONCLUSION: Evaluation of spinal inflammation by MRI may omit lesions in up to 20% of inflamed thoracic vertebrae if both scanning and image assessment do not include sagittal slices that extend to the lateral edges of all vertebrae.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".