Bibliographic record
Abstract
Psoriatic arthritis (PsA) is a chronic, inflammatory musculoskeletal disease that often develops in individuals with psoriasis (PsO), typically following an average latency period of 7 years. Without treatment, PsA can lead to irreversible joint damage, functional impairment, and a range of comorbidities. Despite therapeutic advances, only a minority of patients achieve sustained remission, highlighting the need for new approaches, including disease prevention and early interception. This review explores the emerging concept of PsA prevention in individuals with psoriasis, by addressing modifiable risk factors—such as severe skin disease, nail involvement, and obesity—and predictors such as arthralgias and asymptomatic abnormalities on musculoskeletal ultrasound. Notably, PsO patients represent a unique preventive opportunity in rheumatology, as many treatments address both PsO and PsA, potentially minimizing additional therapeutic risks. A recently proposed framework by the European Alliance of Associations for Rheumatology (EULAR) outlines three stages of progression from PsO to PsA, ranging from individuals ‘at higher risk’, to those with ‘subclinical PsA’, and finally to those with ‘clinical PsA’. Findings from observational studies suggest that treatment of modifiable risk factors may reduce PsA incidence, though prospective data remain limited. Subclinical inflammation detected on imaging and the presence of arthralgia may identify individuals at imminent risk who could benefit from escalation of therapy. Nonetheless, further refinement of this population is necessary to avoid overtreatment. Ongoing clinical trials are expected to help clarify whether early intervention can truly intercept PsA and alter its natural history. Ultimately, success in PsA prevention will require multidisciplinary collaboration, refinement of risk stratification, and thoughtful integration of these screening strategies into clinical practice.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.009 |
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".