Deciphering difficult-to-treat psoriatic arthritis (D2T-PsA): a GRAPPA perspective from an international survey of healthcare professionals
Bibliographic record
Abstract
Objectives: This study contributes to the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA)'s effort to define 'difficult-to-treat' PsA (D2T-PsA), leveraging insights of healthcare professionals who are GRAPPA members. The primary objective is to inform GRAPPA's D2T PsA project, ensuring the consensus definition reflects clinical experience and expertise. Methods: An online survey was conducted among GRAPPA's healthcare professionals managing PsA patients. The survey covered demographic details, structured questions, and open-ended queries to gather comprehensive insights into the experts' viewpoints. Results: About 223 physicians completed the survey, comprising 179 (80.2%) rheumatologists and 40 (17.9%) dermatologists. The majority, 184 (82.5%), favoured establishing distinct definitions for D2T-PsA and complex-to-manage PsA (C2M-PsA). Furthermore, 202 (90.5%) supported a definition that includes objective inflammation signs (clinical, laboratory, imaging, among others). However, opinions varied on the criteria for prior treatment failures, with most (93, 41.7%) favouring a definition that includes at least one conventional synthetic disease-modifying anti-rheumatic drug and two or more biological- or targeted-synthetic-DMARDs with different mechanisms of action. Conclusion: The survey reveals a majority opinion among GRAPPA experts favouring the differentiation between D2T-PsA and C2M-PsA, and the inclusion of objective inflammatory markers in these definitions. However, there is less than 50% agreement on the specific treatment failure criteria, particularly regarding the number of therapies needed to classify PsA as D2T. These findings suggest a need for continued discussion to reach a more unified approach in defining D2T-PsA, reflecting the complexity of the condition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".