Psoriatic arthritis phenotype clusters and their association with treatment response: a real-world longitudinal cohort study from the psoriatic arthritis research consortium
Bibliographic record
Abstract
OBJECTIVES: To identify phenotype clusters and their trajectories in psoriatic arthritis (PsA) and examine the association of the clusters with treatment response in a real-world setting. METHODS: In the multicentre PsA Research Consortium (PARC) study, we applied factor analysis of mixed data to reduce dimensionality and collinearity, followed by hierarchical clustering on principal components. We then evaluated the transition of PsA clusters and their response to new immunomodulatory therapy and tumour necrosis factor inhibitor (TNFi). RESULTS: Among 627 patients with PsA, three clusters were identified: mild PsA and psoriasis only (PsO) (Cluster 1, 47.4%), severe PsA and mild PsO (Cluster 2, 34.3%) and severe PsA and severe PsO (Cluster 3, 18.3%). Among 339 patients starting or changing, significant differences in response were observed (mean follow-up of 0.7 years, SD 0.8), with Cluster 3 showing the largest improvements in cDAPSA and PsAID. No differences were found among those starting TNFi (n=218). cDAPSA remission and PsAID patient acceptable symptom state were achieved in 10% and 54%, respectively. Clusters remained stable over time despite treatment changes, though some transitions occurred, notably from Cluster 3 to milder clusters. CONCLUSION: Data-driven clusters with distinct therapy responses identified in this real-world study highlight the extensive heterogeneity in PsA and the central role of psoriasis and musculoskeletal severity in treatment outcomes. Concurrently, these findings underscore the need for better outcome measures, particularly for individuals with lower disease activity.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".