Prevalence of Sacroiliitis in Inflammatory Bowel Disease Using a Standardized Computed Tomography Scoring System
Bibliographic record
Abstract
OBJECTIVE: There is an increasing emphasis on the early identification and treatment of ankylosing spondylitis (AS) of which the hallmark is sacroiliitis. Patients with inflammatory bowel disease (IBD) are at increased risk of AS and often receive computed tomography (CT) scans of their abdomen, affording clinicians the opportunity to determine the presence of sacroiliitis. Previous studies using CT have relied only on the radiologist's gestalt or a nonvalidated adaptation of the modified New York criteria. Our aim is to assess the prevalence of sacroiliitis in IBD using a validated screening tool and to determine how frequently these patients are referred for rheumatologic evaluation. METHODS: Patients with IBD were recruited from an IBD clinic. Control patients were recruited from a urology clinic and were confirmed to be without back pain, spondylitis, psoriasis, colitis, or uveitis by chart review. CT scans were read by 2 blinded readers and sacroiliitis was defined by the presence of ankylosis or a total erosion score of ≥3. RESULTS: CT scans were available in 233 Crohn's disease (CD) patients, 83 ulcerative colitis (UC) patients, and 108 control patients, and sacroiliitis was seen in 15%, 16.9%, and 5.6% of patients, respectively. The prevalence was higher in patients with IBD than in controls (P = 0.007), with no significant difference between CD and UC patients. Of the 49 IBD patients found to have sacroiliitis by CT scan, only 5 had been referred to a rheumatologist. CONCLUSION: There is a 3-fold higher prevalence of sacroiliitis in IBD compared with controls. Despite a growing awareness of this increased prevalence, many patients are not referred to a rheumatologist.
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 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 | 0.002 | 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".