Collaboration in the management of psoriasis and psoriatic arthritis: A survey of joint working in UK clinical practice
Bibliographic record
Abstract
Abstract Background Treatment guidelines for psoriasis and psoriatic arthritis consider all skin and joint domains and recommend collaborative multidisciplinary team (MDT) working. The uptake of joint working in clinical practice for psoriatic disease management has not been well studied. Objectives This United Kingdom (UK) study aimed to provide a better understanding of current working patterns and collaborating specialities, as well as benefits and challenges of combined clinics. Methods An online survey was emailed to dermatology and rheumatology healthcare professionals (HCPs) using professional networks. Results Responses were received between October 2020 and April 2021 ( N = 80); 60.0% of respondents worked in dermatology and 40.0% in rheumatology. Use of combined clinics with dermatology was reported by 40.6% of rheumatology HCPs, including joint (25.0%), parallel (3.1%) and virtual clinics (6.3%), and MDT meetings (6.2%). Similarly, 50.1% of dermatology HCPs reported use of joint (25.0%), parallel (4.2%) and virtual clinics (2.1%), single visits (2.1%), and MDT meetings (16.7%) with rheumatology. Around one‐quarter of respondents collaborated via email, which was also the main method of collaboration with other specialists. Overall, one‐quarter of respondents reported no collaboration in psoriatic disease management. Perceived benefits of combined clinics included shared knowledge, improved patient outcomes and increased patient satisfaction. Challenges included difficulties in aligning clinician time and geographical location, as well as limited ‘buy‐in’ from senior management. Most respondents felt that the COVID‐19 pandemic had partially or significantly impacted combined clinics. Conclusions This study is one of the first to survey collaborative working in psoriatic disease management and the first in the UK. These findings demonstrate the variety of approaches used and a lack of collaborative working by one‐quarter of respondents. Despite the benefits, numerous challenges in establishing formal arrangements exist. More evidence is needed to demonstrate improved patient outcomes with collaborative working and to standardise best practice.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".