Utility of magnetic resonance imaging in Crohn's associated sacroiliitis: A cross‐sectional study
Bibliographic record
Abstract
OBJECTIVE: Prevalence of sacroiliitis in Crohn's disease (CD) is variable depending on defining criteria. This study utilized standardized sacroiliac joint (SIJ) magnetic resonance imaging (MRI) to identify sacroiliitis in CD patients and its association with clinical and serological markers. METHODS: Consecutive adult subjects with CD prospectively enrolled from an inflammatory bowel disease clinic underwent SIJ MRI. Data collected included CD duration, history of joint/back pain, human leukocyte antigen-B27 status, Bath Ankylosing Spondylitis Metrology Index (BASMI), Bath Ankylosing Spondylitis Disease Activity Index, Harvey Bradshaw Index (HBI) for activity of CD, Ankylosing Spondylitis Disease Activity Score, and various serologic markers of inflammation. Three blinded readers reviewed MRIs for active and structural lesions according to the Spondyloarthritis Research Consortium of Canada modules. RESULTS: Thirty-three CD patients were enrolled: 76% female, 80% White, median age 36.4 years (interquartile range 27.2-49.0), moderate CD activity (mean HBI 8.8 ± SD 4.5). Nineteen subjects (58%) reported any back pain, 13 of whom had inflammatory back pain. Four subjects (12%) showed sacroiliitis using global approach and 6 (18%) met Assessment of SpondyloArthritis international Society MRI criteria of sacroiliitis. Older age (mean 51.2 ± SD 12.5 vs. 37.2 ± 14; P = .04), history of dactylitis (50.0% vs. 3.4%, P = .03) and worse BASMI (4.1 ± 0.7 vs. 2.4 ± 0.8, P ≤ .001) were associated with MRI sacroiliitis; no serologic measure was associated. CONCLUSION: There were 12%-18% of CD patients who had MRI evidence of sacroiliitis, which was not associated with back pain, CD activity or serologic measures. This data suggests that MRI is a useful modality to identify subclinical sacroiliitis in CD patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".