Identifying Axial Spondyloarthritis in Patients With Inflammatory Bowel Disease Using Computed Tomography
Bibliographic record
Abstract
OBJECTIVE: The diagnosis of axial spondyloarthritis (axSpA) is hampered by diagnostic delay. Computed tomography (CT) undertaken for nonmusculoskeletal (non-MSK) indications in patients with inflammatory bowel disease (IBD) offers an opportunity to identify sacroiliitis for prompt rheumatology referral. This study aims to identify what proportion of patients with IBD who underwent abdominopelvic CT for non-MSK indications have axSpA and to explore the role of a standardized screening tool to prospectively identify axSpA on imaging. METHODS: Abdominopelvic CT scans of patients with verified IBD, aged 18 to 55 years, performed for non-MSK indications were reviewed by radiologists for the presence of CT-defined sacroiliitis (CTSI), using criteria from a validated CT screening tool. All patients identified were sent a screening questionnaire, and those with self-reported chronic back pain (CBP), CBP duration of greater than 3 months, and age of onset of less than 45 years were invited for rheumatology review. RESULTS: CTSI was identified in 60 out of 301 (19.9%) patients. Out of these 60 patients, 32 (53%) responded to an invitation to participate, and 27 out of 32 (84.3%) were enrolled. Of these, 8 had a preexisting axSpA diagnosis and 5 did not report CBP. In total, 14 patients underwent rheumatology assessment, and 3 out of 14 (21.4%, 95% CI 4.7-50.8) had undiagnosed axSpA. In total, 11 out of 27 (40.7%, 95% CI 22.4-61.2) patients had a rheumatologist-verified diagnosis of axSpA. CONCLUSION: In this study, 5% (3/60) of patients with IBD undergoing abdominopelvic CT for non-MSK indications with CTSI were found to have undiagnosed axSpA and, overall, 18.3% (11/60) were found to have axSpA. This reveals a significant hidden population of axSpA and highlights the need for a streamlined pathway from sacroiliitis detection to rheumatology referral.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".