Utilization of the Psoriasis Epidemiology Screening Tool (PEST): A Risk Stratification Strategy for Early Referral of Psoriatic Arthritis Patients to Minimize Irreversible Erosive Joint Damage
Bibliographic record
Abstract
Psoriatic arthritis (PsA) can affect a diverse range of anatomical sites and its heterogeneous presentation contributes to misdiagnosis and delayed treatment with conventional and biologic disease-modifying antirheumatic drugs (DMARDs). Up to 15% of psoriasis (PsO) patients affected by PsA remain undiagnosed. Early detection and referral to a rheumatologist are crucial to optimize care and minimize irreversible erosive joint damage. To improve the rheumatology referral process, the authors propose a risk stratification tool to identify and triage patients with possible psoriatic arthritis. With the aim of ultimately assisting in early treatment initiation, this risk stratification algorithm can be used in both dermatology and primary care clinics. It is based on the Psoriasis Epidemiology Screening Tool (PEST) combined with the ClASsification criteria for Psoriatic Arthritis (CASPAR). This article intends to provide a rationale for further prospective studies whose objective would be to validate this screening algorithm.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".