Assessment of the Toronto Psoriatic Arthritis Screen 2 as a Screening Tool for Psoriasis
Bibliographic record
Abstract
BACKGROUND: Psoriasis is a chronic inflammatory disease affecting multiple organ systems and resulting in reduced quality of life for many patients. A screening tool would be useful, particularly in underserviced or research settings with limited access to dermatologists. The Toronto Psoriatic Arthritis Screen, version 2 (ToPAS 2) is a validated screening tool for psoriatic arthritis containing questions specific for psoriasis. OBJECTIVES: To evaluate the performance of skin-specific questions from ToPAS 2 for the diagnosis of psoriasis. METHODS: Participants aged >18 were recruited from Dermatology and Family Medicine clinics and completed the ToPAS 2 questionnaire prior to being examined by a dermatologist for psoriasis. Two scoring indexes were derived from the ToPAS 2 skin-related questions using backward selection regression models. Statistical analysis was performed using receiver operating characteristic (ROC) curves to measure their performances. RESULTS: Two hundred and fifty eight participants were recruited. 32 (12%) were diagnosed with psoriasis by dermatologist assessment. Index 1 includes all 5 skin-related questions from ToPAS 2, while Index 2 includes three of the five questions. Both indexes demonstrate high specificity (82% to 92%), sensitivity (69% to 84%), and excellent negative predictive value (NPV) (>95%) for a diagnosis of psoriasis. The overall discriminatory power of these models is 0.823 (Index 1) and 0.875 (Index 2). CONCLUSIONS: Skin-related questions from ToPAS 2 have discriminatory value in detecting psoriasis, specifically questions relating to a family history, a prior physician diagnosis of psoriasis or a rash consistent with images of plaque psoriasis. This study is a valuable step in developing a screening tool for psoriasis.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".