An international multicentre analysis of current prescribing practices and shared decision-making in psoriatic arthritis
Bibliographic record
Abstract
OBJECTIVES: Shared decision-making (SDM) is advocated to improve patient outcomes in PsA. We analysed current prescribing practices and the extent of SDM in PsA across Europe. METHODS: The ASSIST study was a cross-sectional observational study of PsA patients ≥18 years of age attending face-to-face appointments between July 2021 and March 2022. Patient demographics, current treatment and treatment decisions were recorded. SDM was measured by the clinician's effort to collaborate (CollaboRATE questionnaire) and patient communication confidence (PEPPI-5 tool). RESULTS: A total of 503 patients were included from 24 centres across the UK, France, Germany, Italy and Spain. Physician- and patient-reported measures of disease activity were highest in the UK. Conventional synthetic DMARDs constituted a higher percentage of current PsA treatment in the UK than continental Europe (66.4% vs 44.9%), which differed from biologic DMARDs (36.4% vs 64.4%). Implementing treatment escalation was most common in the UK. CollaboRATE and PEPPI-5 scores were high across centres. Of 31 patients with low CollaboRATE scores (<4.5), no patients with low PsAID-12 scores (<5) had treatment escalation. However, of 465 patients with CollaboRATE scores ≥4.5, 59 patients with low PsAID-12 scores received treatment escalation. CONCLUSIONS: Higher rates of treatment escalation seen in the UK may be explained by higher disease activity and a younger cohort. High levels of collaboration in face-to-face PsA consultations suggests effective implementation of the SDM approach. Our data indicate that in patients with mild disease activity, only those with higher perceived collaboration underwent treatment escalation. Prospective studies should examine the impact of SDM on PsA patient outcomes. TRIAL REGISTRATION: clinicaltrials.gov, NCT05171270.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".