Screening and Referral Strategies for the Early Recognition of Psoriatic Arthritis Among Patients With Psoriasis: Results of a GRAPPA Survey
Bibliographic record
Abstract
OBJECTIVE: This study aimed to explore the experiences of dermatologists and rheumatologists in the early recognition of psoriatic arthritis (PsA) and to identify potential improvements to the current shared-care model. METHODS: A 24-question survey addressing referral strategies was constructed by the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) project steering committee and sent to all members (n = 927). Questions addressed the use of screening tools, frequency of PsA in patients with psoriasis, therapeutic decision making, and suggestions for earlier PsA recognition and current unmet needs. RESULTS: There were 149 respondents (16.1% response rate), which included 113 rheumatologists from 37 countries and 26 dermatologists from 16 countries. Of the dermatologists, 81% use PsA-specific screening instruments. Conversely, rheumatologists reported that only 26.8% of patients referred to them from all sources had been assessed with screening tools. Although dermatologists reported that a mean of 67% of suspected PsA cases were confirmed, rheumatologists reported a mean of 47.9% of confirmed cases. Both specialties reported similar views regarding optimization of the diagnostic process and indicated that the best approach involved combining patient-reported (ie, screening tools) and physician-confirmed findings. Moreover, both specialties identified the education of primary care physicians (PCPs) and dermatologists as the greatest priority to improve PsA screening. CONCLUSION: The survey indicated the current unmet needs in the early recognition of PsA. Important areas to address include improving the use of screening instruments, increasing the education of community-based dermatologists and PCPs, and using a combination of patient-reported and physician-confirmed findings in the screening approach.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".