The Electronic Psoriasis and Arthritis Screening Questionnaire (ePASQ): A Sensitive and Specific Tool to Diagnose Psoriatic Arthritis Patients
Bibliographic record
Abstract
BACKGROUND: We report on an electronic version of the Psoriatic Arthritis Screening Questionnaire (ePASQ), a sensitive and specific tool for diagnosis of psoriatic arthritis (PsA) in patients with plaque psoriasis. OBJECTIVE: To validate the ePASQ against the original paper version. METHOD: The ePASQ scores 15 points on 10 weighted questions and a 68-joint diagram. Data were collected from a prospective cohort of 42 patients with early PsA meeting the Classification Criteria for Psoriatic Arthritis (CASPAR) criteria and from 12 plaque psoriasis patients without PsA. RESULTS: The receiver operating characteristic curves for the ePASQ group yielded an optimal 97.62% sensitivity and 75.00% specificity, for a cutoff score of 7. A cutoff point of 8 yielded 88.10% sensitivity and 75.00% specificity. Concordance of the paper and electronic scores was very high. CONCLUSION: The ePASQ is a sensitive and specific tool to screen for PsA. The simple electronic administration and automatic scoring minimize clinician involvement and increase the potential for wider distribution.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".