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 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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".