Utilization of the validated Psoriasis Epidemiology Screening Tool to identify signs and symptoms of psoriatic arthritis among those with psoriasis: a cross‐sectional analysis from the <scp>US</scp>‐based Corrona Psoriasis Registry
Bibliographic record
Abstract
BACKGROUND: Despite increasing awareness of the disease, rates of undiagnosed psoriatic arthritis (PsA) are high in patients with psoriasis (PsO). The validated Psoriasis Epidemiology Screening Tool (PEST) is a five-item questionnaire developed to help identify PsA at an early stage. OBJECTIVES: To assess the risk of possible undiagnosed PsA among patients with PsO and characterize patients based on PEST scores. METHODS: This study included all patients enrolled in the Corrona PsO Registry with data on all five PEST questions. Demographics, clinical characteristics and patient-reported outcomes were compared in Corrona PsO Registry patients with PEST scores ≥3 and <3 using t-tests for continuous variables and chi-squared tests for categorical variables; scores ≥3 may indicate PsA. RESULTS: Of 1516 patients with PsO, 904 did not have dermatologist-reported PsA; 112 of these 904 patients (12.4%) scored ≥3 and were significantly older, female, less likely to be working, and had higher BMI than patients with scores <3. They also had significantly longer PsO duration, were more likely to have nail PsO and had worse health status, pain, fatigue, Dermatology Life Quality Index and activity impairment. CONCLUSIONS: Improved PsA screening is needed in patients with PsO because the validated PEST identified over one-tenth of registry patients who were not noted to have PsA as having scores ≥3, who could have had undiagnosed PsA. Appropriate, earlier care is important because these patients were more likely to have nail PsO, worse health-related quality of life and worse activity impairment.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".