Understanding the transition from psoriasis to psoriatic arthritis: the role of targeted therapy
Bibliographic record
Abstract
Most individuals who develop psoriatic arthritis (PsA) first present with psoriasis (PsC), often years before musculoskeletal symptoms emerge. Progression from PsC to PsA is multifactorial, shaped by genetic and environmental influences, and may involve at-risk stages and subclinical stages before culminating in clinically overt arthritis. Consistently identified risk factors include greater PsC severity, nail involvement, arthralgia, and elevated body mass index. In this Viewpoint, we review and critically appraise current evidence on PsC-to-PsA transition, focusing on how the mechanism of action of biologic disease-modifying antirheumatic drugs may influence this trajectory. Emerging data suggest that therapies targeting the interleukin (IL)-23/IL-17 axis may provide greater protection against PsA development than tumour necrosis factor inhibitors, underscoring the central role of this pathway in psoriatic disease pathogenesis. However, existing studies are limited by confounding, protopathic bias, and heterogeneous cohorts. By integrating mechanistic insights with clinical data, we emphasise the urgent need for rigorously designed multinational randomised controlled trials to determine whether, and how, biologic therapy can modify the natural history of psoriatic disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".