A Practical Guide for Assessment of Skin Burden in Patients With Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Rheumatologists play a pivotal role in the management of patients with psoriatic arthritis (PsA). Due to time constraints during clinic visits, the skin may not receive the attention needed for optimal patient outcome. Therefore, the aim of this study was to select a set of core questions that can help rheumatologists in daily rheumatology clinical practice to identify patients with PsA with a high skin burden. METHODS: Baseline data from patients included in the Dutch South West Psoriatic Arthritis (DEPAR) cohort were used. Questions were derived from the Skindex-17 and Dermatology Life Quality Index (DLQI) questionnaires. Underlying clusters of questions were identified with an exploratory principal component analysis (PCA) with varimax rotation, after which a 2-parameter logistic model was fitted per cluster. Questions were selected based on their discrimination and difficulty. Subsequently, 2 flowcharts were made with categories of skin burden severity. Clinical considerations were specified per category. RESULTS: In total, 413 patients were included. The PCA showed 2 underlying clusters: a psychosocial domain and a domain assessing physical symptoms. We selected these 2 domains. The psychosocial domain contains 3 questions and specifies 4 categories of skin burden severity. The physical symptoms domain contains 2 questions and categorizes patients in 1 out of 3 categories. CONCLUSION: We have selected a set with a maximum of 5 questions that rheumatologists can easily implement in their consultation to assess skin burden in patients with PsA. This practical guide makes the assessment of skin burden more accessible to rheumatologists and can aid in clinical decision making.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.034 |
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".