Limitations in Screening Instruments for Psoriatic Arthritis: A Comparison of Instruments in Patients with Psoriasis
Bibliographic record
Abstract
OBJECTIVE: To compare the abilities of 3 validated screening instruments to predict the diagnosis of psoriatic arthritis (PsA) in patients with psoriasis. METHODS: Prior to a rheumatologic evaluation, 213 participants in the Utah Psoriasis Initiative completed the Psoriasis Epidemiology Screening project (PEST), the Toronto Psoriatic Arthritis Screen (ToPAS), and the Psoriatic Arthritis Screening and Evaluation (PASE). Previously established instrument cutoff scores were used to designate positive and negative classifications. Sensitivities and specificities were determined by comparing instrument classifications to the rheumatologist's diagnosis. Phenotypic features and alternative diagnoses were compared between participants who screened positively and negatively on each instrument. Discrepancies between the rheumatologist's examination findings and responses to specific instrument questions were compared. RESULTS: The sensitivities of PEST, ToPAS, and PASE were 85%, 75%, and 68%, and the specificities were 45%, 55%, and 50%, respectively. The instruments were less sensitive in patients with lower disease activity, fewer PsA features, and shorter disease duration. The instruments did not consistently differentiate between PsA and other types of musculoskeletal disease. Discrepancies between examination findings and responses to instrument questions occurred more frequently with ToPAS than with PEST and PASE. CONCLUSION: Sensitivities and specificities for PEST, ToPAS, and PASE were lower than previously reported. This population included patients with PsA and other types of musculoskeletal disease and may represent those most likely to complete a screening instrument and follow through with a rheumatology referral. Further analyses may enable the development of more successful screening strategies for PsA in psoriasis patients with musculoskeletal complaints.
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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 | 0.038 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".