Diagnosis and Assessment of Psoriasis for the Rheumatologist: A Workshop From the GRAPPA 2024 Annual Meeting
Bibliographic record
Abstract
Rheumatologists and other nondermatologists often encounter patients with psoriatic arthritis (PsA) who present with cutaneous diseases that mimic psoriasis (PsO). Cutaneous disorders including tinea, seborrheic dermatitis, eczema, pityriasis rubra pilaris, syphilis, or cutaneous lymphoma are commonly mistaken for PsO. It is crucial for rheumatologists and other nondermatologists to recognize alternative conditions and to consider referral to dermatology when skin disease is not responding to therapy. Correct diagnosis is important when assessing disease severity in clinical practice as well. Although the Psoriasis Area and Severity Index (PASI) and the Dermatology Life Quality Index (DLQI) are gold standards for physician- and patient-reported outcomes in clinical trials, they are not practical to deploy in busy clinical practice. Use of a physician global assessment (PGA), body surface area using a handprint method, and informal patient-reported outcomes can be useful in documenting the burden of disease. A treat-to-target approach using a PGA of clear/almost clear is ideal. At the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2024 annual meeting, a 2-part workshop was conducted for rheumatologists to first review skin disorders commonly mistaken for PsO, and second, to review outcome measures best suited for clinical practice.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".