Features of Axial Spondyloarthritis in Two Multicenter Cohorts of Patients with Psoriasis, Uveitis, and Colitis Presenting with Undiagnosed Back Pain
Bibliographic record
Abstract
OBJECTIVE: We aimed to assess the following: (1) the frequency of axial spondyloarthritis (axSpA) according to extra-articular presentation and HLA-B27 status, (2) clinical and imaging features that distinguish axSpA from non-axSpA, and (3) the impact of magnetic resonance imaging (MRI) on diagnosis and classification of axSpA. METHODS: The Screening for Axial Spondyloarthritis in Psoriasis, Iritis, and Colitis (SASPIC) study enrolled patients in two multicenter cohorts. Consecutive patients with undiagnosed chronic back pain attending dermatology, ophthalmology, and gastroenterology clinics with psoriasis (PsO), acute anterior uveitis (AAU), or inflammatory bowel disease (IBD) were referred to a local rheumatologist with special expertise in axSpA for a structured diagnostic evaluation. The primary outcome was the proportion of patients diagnosed with axSpA by the final global evaluation. RESULTS: Frequency of axSpA was 46.7%, 61.6%, and 46.8% in patients in SASPIC-1 (n = 212) and 23.5%, 57.9%, and 23.3% in patients in SASPIC-2 (n = 151) with PsO, AAU, or IBD, respectively. Among those who were B27 positive, axSpA was diagnosed in 70%, 74.5%, and 66.7% of patients in SASPIC-1 and in 71.4%, 87.8%, and 55.6% of patients in SASPIC-2 with PsO, AAU, or IBD, respectively. All musculoskeletal clinical features were nondiscriminatory. MRI was indicative of axSpA in 60% to 80% of patients and MRI in all patients (SASPIC-2) versus on-demand (SASPIC-1) led to 25% fewer diagnoses of axSpA in patients who were HLA-B27 negative with PsO or IBD. Performance of the Assessment of SpondyloArthritis International Society classification criteria was greater with routine MRI (SASPIC-2), though sensitivity was lower than previously reported. CONCLUSION: Optimal management of patients presenting with PsO, AAU, IBD, and undiagnosed chronic back pain should include referral to a rheumatologist. Conducting MRI in all patients enhances diagnostic accuracy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".