Clinical and Patient-reported Outcomes in Patients with Psoriatic Arthritis (PsA) by Body Surface Area Affected by Psoriasis: Results from the Corrona PsA/Spondyloarthritis Registry
Bibliographic record
Abstract
Objective. Psoriatic arthritis (PsA) is commonly comorbid with psoriasis; the extent of skin lesions is a major contributor to psoriatic disease severity/burden. We evaluated whether extent of skin involvement with psoriasis [body surface area (BSA) > 3% vs ≤ 3%] affects overall clinical and patient-reported outcomes (PRO) in patients with PsA. Methods. Using the Corrona PsA/Spondyloarthritis Registry, patient characteristics, disease activity, and PRO at registry enrollment were assessed for patients with PsA aged ≥ 18 years with BSA > 3% versus ≤ 3%. Regression models were used to evaluate associations of BSA level with outcome [modified minimal disease activity (MDA), Health Assessment Questionnaire (HAQ) score, patient-reported pain and fatigue, and the Work Productivity and Activity Impairment questionnaire score]. Adjustments were made for age, sex, race, body mass index, disease duration, and history of biologics, disease-modifying antirheumatic drug, and prednisone use. Results. This analysis included 1240 patients with PsA with known BSA level (n = 451, BSA > 3%; n = 789, BSA ≤ 3%). After adjusting for potential confounding variables, patients with BSA > 3% versus ≤ 3% had greater patient-reported pain and fatigue and higher HAQ scores (p = 2.33 × 10−8, p = 0.002, and p = 1.21 × 10−7, respectively), were 1.7× more likely not to be in modified MDA (95% CI 1.21–2.41, p = 0.002), and were 2.1× more likely to have overall work impairment (1.37–3.21, p = 0.0001). Conclusion. These Corrona Registry data show that substantial skin involvement (BSA > 3%) is associated with greater PsA disease burden, underscoring the importance of assessing and effectively managing psoriasis in patients with PsA because this may be a contributing factor in PsA severity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".