Risk of Spondyloarthritis in Patients With Inflammatory Bowel Disease Receiving Treatment With Biologics or Janus Kinase Inhibitors
Bibliographic record
Abstract
Objective To detect spondyloarthritis (SpA) and evaluate risk factors in patients with inflammatory bowel disease (IBD) during biologic or Janus kinase inhibitor (JAKi) treatment. Methods This was a retrospective cohort study of patients with IBD receiving biologics or JAKi, excluding prior SpA cases. We identified patients who developed musculoskeletal (MSK) symptoms during IBD treatment. SpA was diagnosed after a clinical evaluation by a rheumatologist alongside imaging analysis of conventional radiographs and HLA-B27 determination. Magnetic resonance imaging of the sacroiliac joints was performed only in cases where the conventional radiograph was inconclusive. Results Of 1649 patients with IBD receiving biologic or JAKi treatment (Crohn disease: 1335; ulcerative colitis [UC]: 314), 96 (5.8%) were excluded due to a prior SpA diagnosis. Among the remaining 1553 patients, 106 (6.8%) developed MSK symptoms during IBD treatment, and 30 (1.9%) were diagnosed with SpA (axial: 20; peripheral: 10) during the follow-up (median 5.2 [IQR 3.4-7.5] years). Risk factors for SpA in these patients included a partial Mayo score for UC at the time of onset of MSK symptoms (hazard ratio [HR] 1.57; P = 0.03) and HLA-B27 positivity (HR 3.70; P = 0.004). As well as IBD treatment, 23/30 (77%) patients with SpA used nonsteroidal antiinflammatory drugs (NSAIDs). IBD disease activity did not worsen during treatment, regardless of NSAID use. Conclusion During a median follow-up of 5.2 years, 6.8% of patients with IBD undergoing biologic or JAKi treatment developed MSK symptoms, with one-third subsequently diagnosed with SpA. HLA-B27 positivity and higher UC disease activity were associated with an increased risk of SpA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".