Characterising axial psoriatic arthritis: correlation between whole spine MRI abnormalities and clinical, laboratory and radiographic findings
Bibliographic record
Abstract
OBJECTIVE: To describe the prevalence of inflammatory and structural lesions using whole spine MRI in patients with psoriatic disease, and to assess their correlation with clinical features and with axial spondyloarthritis (axSpA) classification criteria. METHODS: This retrospective analysis included patients with whole spine and sacroiliac joints (SIJ) MRI, selected from 2 populations: (1) active psoriatic arthritis (PsA), irrespective of axial symptoms; (2) psoriasis with confirmed or suspected PsA and axSpA symptoms. MRI spondylitis and/or sacroiliitis (MRI-SpA) was defined according to Assessment of Spondyloarthritis International Society (ASAS) consensus and by radiologist impression. Agreement between MRI-SpA and different inflammatory back pain (IBP) definitions (Berlin/ASAS/rheumatologist criteria) and the axSpA classification criteria were calculated considering MRI as gold standard. Logistic regression determined MRI-SpA-associated factors. RESULTS: 93 patients were analysed (69.9% PsA; 30.1% psoriasis). Back pain was present in 81.7%, defined as IBP in 36.6%-57%. MRI-SpA was found in 9.7% of patients by ASAS definition and in 12.9% by radiologist impression, of which 25% had isolated spondylitis.Low agreement was found between the three IBP definitions and MRI-SpA. Rheumatologist criteria was the most sensitive (50%-55.6%) while ASAS and Berlin criteria were the most specific (61.9%-63%). axSpA criteria had poor sensitivity for MRI-SpA (22.2%-25%). Late onset of back pain or asymptomatic patients accounted for most cases with MRI-SpA not meeting axSpA or IBP criteria. Male sex was associated with MRI-SpA (OR 6.91; 95% CI 1.42 to 33.59) in multivariable regression analysis. CONCLUSION: Prevalence of MRI-defined axSpA was low and showed poor agreement with IBP and axSpA criteria.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".