Comparison of three screening tools to detect psoriatic arthritis in patients with psoriasis (CONTEST study)
Bibliographic record
Abstract
BACKGROUND: Multiple questionnaires to screen for psoriatic arthritis (PsA) have been developed but the optimal screening questionnaire is unknown. OBJECTIVES: To compare three PsA screening questionnaires in a head-to-head study using CASPAR (the Classification Criteria for Psoriatic Arthritis) as the gold standard. METHODS: This study recruited from 10 U.K. secondary care dermatology clinics. Patients with a diagnosis of psoriasis, not previously diagnosed with PsA, were given all three questionnaires. All patients who were positive on any questionnaire were invited for a rheumatological assessment. Receiver operating characteristic (ROC) curves were used to compare the sensitivity, specificity and area under the curve of the three questionnaires according to CASPAR criteria. RESULTS: In total, 938 patients with psoriasis were invited to participate and 657 (70%) patients returned the questionnaires. One or more questionnaires were positive in 314 patients (48%) and 195 (62%) of these patients attended for assessment. Of these, 47 patients (24%) were diagnosed with PsA according to the CASPAR criteria. The proportion of patients with PsA increased with the number of positive questionnaires (one questionnaire, 19·1%; two, 34·0%; three, 46·8%). Sensitivities and specificities for the three questionnaires, and areas under the ROC curve were, respectively: Psoriatic Arthritis Screening Evaluation (PASE), 74·5%, 38·5%, 0·594; Psoriasis Epidemiology Screening Tool (PEST), 76·6%, 37·2%, 0·610; Toronto Psoriatic Arthritis Screen (ToPAS), 76·6%, 29·7%, 0·554. The majority of patients with a false positive response had degenerative or osteoarthritis. CONCLUSION: Although the PEST and ToPAS questionnaires performed slightly better than the PASE questionnaire at identifying PsA, there is little difference between these instruments. These screening tools identify many cases of musculoskeletal disease other than 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".