Association of Contextual Factors With Sonographic Inflammatory and Structural Phenotypes in Patients With Psoriatic Arthritis: A Cross-Sectional Study
Bibliographic record
Abstract
Objective Ultrasound (US) can enhance psoriatic arthritis (PsA) disease activity assessment, but the effect of contextual factors on sonographic findings in PsA remains unclear. This study examined how demographic and clinical factors affect sonographic lesions in active PsA. Methods This was a cross-sectional study of 115 patients with active PsA who underwent US evaluation for synovitis, enthesitis, paratenonitis, tenosynovitis, joint bone erosion, and new bone formation (NBF). Lesions were scored semiquantitatively with B-mode and Doppler using a 64-joint, 16-enthesis, and 34-tendon US protocol. Total scores were analyzed using t tests and linear regression by age, sex, BMI, diabetes, alcohol, smoking, disease duration, and biologic/targeted synthetic disease-modifying antirheumatic drug (b/tsDMARD) exposure. Results Patients (mean age 47.2, 48% female) had a mean Disease Activity Index for PsA of 22.7 (SD 12.9) and mean sonographic scores for synovitis and enthesitis of 35.6 (SD 22.9) and 30.1 (SD 22.1), respectively. Older patients showed significantly higher enthesitis, bone erosion, and NBF scores. Multivariable analysis revealed that age ≥ 60 years was linked to significantly higher inflammatory and structural enthesitis (adjusted β 6.37 and 14.6, respectively), bone erosion (β 2.53), and NBF (β 13.7) scores, and that b/tsDMARD exposure correlated with significantly higher synovitis (β 12.8) and tenosynovitis scores (β 5.95). Conclusion Older age correlated with more severe inflammatory and structural lesions, reflecting either a more severe PsA phenotype or overlap with age-related changes. Higher synovitis and tenosynovitis scores in b/tsDMARD-exposed patients likely reflect disease severity rather than a direct effect of treatment. Incorporating contextual factors into sonographic assessments can improve personalized PsA management.
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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 | 0.000 |
| 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".