Deciphering difficult-to-treat psoriatic arthritis: insights from an international survey of patients with psoriatic arthritis
Bibliographic record
Abstract
OBJECTIVES: Psoriatic arthritis (PsA) is a heterogeneous inflammatory disease in which a significant proportion of patients remain refractory to existing therapies. The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) initiated a project aimed at unravelling the reasons for treatment failures in PsA, culminating in the establishment of definitions for difficult-to-treat PsA (D2T-PsA) and complex-to-manage PsA (C2M-PsA). This study explores patient perspectives on treatment-resistant PsA, incorporating a broader patient perspective into the overarching GRAPPA project. METHODS: A multilingual (10 languages), online survey to explore PsA patients' perspectives on treatment inefficacies was used. It was developed collaboratively by GRAPPA members and patient research partners. It included sections on demographic data, structured questions about treatment failures, and open-ended questions. Data analysis used descriptive statistics and inductive coding of qualitative responses via Dedoose. RESULTS: Among 570 respondents, most were female (68.8%) and White (72.6%), with an average PsA diagnosis delay of 4.3 years. Key contributors to D2T- and C2M-PsA were persistent joint pain and psoriasis (65.7%), fatigue (52.8%) and medication side effects (41.7%). Ranked by impact, arthritis was the most debilitating symptom. Quality of life concerns were notable, with sleep impairment and reduced life enjoyment being reported by 66.4%. Language differences emerged; for instance, Dutch and Italian respondents prioritized fatigue and daily life impact, respectively. CONCLUSION: This is the first international study to highlight patient-driven insights in the management of resistant PsA, emphasizing a multidimensional approach that considers biological and psychosocial factors. These insights will inform the ongoing GRAPPA initiative to standardize definitions for treatment-resistant PsA, ultimately improving patient care.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".