Reliability and Sensitivity of a New Simple Screening Test (TUPAST) in Psoriatic Arthritis Including Axial Involvement: Methodological Study
Bibliographic record
Abstract
Objective: Early detection of psoriatic arthritis (PsA) can prevent destruction and functional disabilities. Dermatologists play an important role in the early diagnosis and treatment of PsA. The aim of the study was to develop a practical questionnaire that does not take long time for early diagnosis of PsA and for not to overlook axial involvement. Material and Methods: This was a prospective study including 200 psoriasis patients. Turkish Psoriatic Arthritis Screening Tool (TUPAST) questions were designed in a simple and plain language that the patients could easily understand. Patients were asked to answer these 6 questions and the well-known questionnaire Toronto Psoriatic Arthritis Screening 2 (ToPAS 2) synchronously. Results: ROC analysis was performed to determine the cut-off value of TUPAST, and the cut-off value was determined as 3. The sensitivity of the cut-off value was calculated as 54.32% and the specificity as 90.68%. The cut-off value obtained for ToPAS 2 was 8 and its sensitivity was 79%, and specificity was 55% in our patient population. There was a significant difference between two tests in terms of time spent for answering questions (TUPAST-0.5 minute, ToPAS 2-3.6 minute) (p<0.05). Conclusion: PsA screening by dermatologist can be the first step in diagnosis of joint involvement in psoriasis. Due to the heavy patient traffic of dermatology outpatient clinics, we need tests that do not take much time. TUPAST is a simple and time saving screening test that takes only 30 seconds to answer and can be used in prediagnosis of PsA.
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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 | 0.026 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".