Distinctive clinical patterns and management trends in late-onset psoriatic arthritis: data from Argentina’s RECCAPSO registry
Bibliographic record
Abstract
Background: Late-onset psoriatic arthritis (LO-PsA) has been underexplored despite its growing prevalence in aging populations. Understanding its distinct clinical presentation and treatment patterns is essential to optimize care in this subgroup. Objectives: To describe the demographic, clinical, and therapeutic features of patients with LO-PsA compared to early onset PsA (EO-PsA) using data from the Argentine RECCAPSO registry. Design: Ambispective, multicenter analysis with a cross-sectional evaluation. Methods: Patients with PsA were categorized into EO-PsA (age of onset ⩽60 years) and LO-PsA (>60 years). Demographics, clinical characteristics, disease activity, and treatment variables were compared between groups using appropriate statistical tests. A multivariate logistic regression model was constructed to identify factors independently associated with LO-PsA. Results: A total of 271 PsA patients were included (EO-PsA: n = 184; LO-PsA: n = 87). LO-PsA patients had higher frequencies of hypertension (50% vs 21.4%, p < 0.001), diabetes (22.1% vs 7.9%, p = 0.007), and oligoarticular presentation (57.4% vs 40.5%, p = 0.03), and were less likely to receive b/tsDMARDs (42.6% vs 58.7%, p = 0.02). In multivariate analysis, hypertension (OR: 4.69, 95% CI: 1.83–12.03) and diabetes (OR: 14.83, 95% CI: 2.36–93.05) were independently associated with LO-PsA. Conclusion: LO-PsA presents a distinct clinical profile characterized by greater comorbidity burden and lower exposure to advanced therapies. These findings highlight the need for tailored management strategies in older adults with PsA.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".