The Performance of Psoriatic Arthritis Screening Questionnaires in Patients with Psoriasis
Bibliographic record
Abstract
Several screening questionnaires have been developed to identify patients with psoriatic arthritis (PsA) in the psoriasis population in dermatology and general practice settings1,2,3,4,5. However, the diagnosis of PsA using these questionnaires is a topic for debate, partly because of disease heterogeneity, and the complications from inconsistent performance results6,7,8,9. We aimed to evaluate the performance of the Psoriatic Arthritis Screening and Evaluation tool (PASE)1,2, the Psoriasis Epidemiology Screening Tool (PEST)2, the Toronto Psoriatic Arthritis Screen 2 (ToPAS 2)3, and the Early Arthritis for Psoriatic Patients (EARP)4 questionnaires in diagnosing PsA and to test the performance of the CONTEST questionnaire5 compared to the other existing tools. Patients with psoriasis from the dermatology and combined rheumatology-dermatology clinics in one medical center completed the PASE, ToPAS 2, PEST, and EARP questionnaires in random sequence prior to rheumatologic evaluation. The PASE, ToPAS 2, PEST, and EARP questionnaires were translated from English to Hebrew by a professional translator after the appropriate institutions gave approval to use these questionnaires. … Address correspondence to Dr. A. Haddad, Rheumatology Unit, Carmel Medical Center, 7 Michal St., Haifa 34362, Israel. E-mail: haddadamir{at}yahoo.com.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".