Managing Musculoskeletal Symptoms in Patients With Psoriasis: Who Should Be in the Driver’s Seat?
Bibliographic record
Abstract
The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2024 annual meeting included a lively debate regarding the optimal management of musculoskeletal (MSK) symptoms in patients with psoriasis (PsO) at risk of or with early psoriatic arthritis (PsA). Drs. Fabian Proft and Laura Savage presented comprehensive, evidence-based retrospective arguments from the perspectives of rheumatology and dermatology. Proft advocated for rheumatologists to lead PsA management by highlighting the specialized training that allows rheumatologists to identify inflammatory diseases and use advanced imaging techniques to differentiate PsA from mechanical MSK conditions. In contrast, Savage emphasized the pivotal role of dermatologists, who often serve as the first healthcare providers (HCPs) to encounter emergent PsA in their patients with PsO. Dermatologists are increasingly aware of the importance of early detection and timely intervention, as well as of the new data that support the concept of "treating to intercept" in patients at risk of transition from PsO to PsA. Both experts highlighted systemic barriers hindering collaborative care and underscored the necessity of patient-centered approaches that effectively address skin and joint manifestations. This article summarizes the insightful debate, reinforcing the importance of a multidisciplinary approach to optimize patient outcomes with PsA.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".