Inflammatory bowel disease does not confer higher disease activity or greater radiographic progression in axial spondyloarthritis
Bibliographic record
Abstract
OBJECTIVES: To examine whether inflammatory bowel disease (IBD) influences disease activity and radiographic progression in patients with axial spondyloarthritis (axSpA). METHODS: This was a longitudinal cohort study of axSpA patients from a tertiary referral centre. Active axSpA was assessed using the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI ≥4) in a dataset of 1268 patients. Radiographic progression, defined as a ≥ 2-unit increase in the modified Stoke Ankylosing Spondylitis Score (mSASSS) over 2 years, was evaluated in a subset of 471 patients with available longitudinal radiographic data. Generalized estimating equation models were used to assess associations between IBD and each outcome, adjusting for key confounders. RESULTS: Among 1451 axSpA patients, 184 (13%) had IBD. Compared with axSpA-only patients, those with IBD had lower HLA-B27 positivity and higher rates of uveitis, peripheral arthritis and elevated C-reactive protein. However, IBD was not associated with an increased risk of active axSpA (OR 0.98, 95% CI 0.75-1.28) or radiographic progression (OR 1.45, 95% CI 0.78-2.73). Sensitivity analyses accounting for time-varying IBD status confirmed these findings. CONCLUSION: AxSpA-IBD patients exhibited distinct clinical features but did not have worse disease activity or greater radiographic progression than those with axSpA alone. These findings suggest that while IBD and axSpA share overlapping immunopathogenic mechanisms, IBD does not exacerbate axSpA severity. Further research is needed to explore the effects of IBD severity and duration on long-term axSpA outcomes to delineate the interaction between these two diseases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".