Toward Harmonized Recommendations for Psoriatic Arthritis: A Comparative Viewpoint on Global Guidelines
Bibliographic record
Abstract
OBJECTIVE: Psoriatic arthritis (PsA) guidelines aim to provide consistent, evidence-based recommendations. Multiple regional guidelines exist, often based on similar evidence but with different methodologies and contexts. Our aim was to compare recent PsA treatment guidelines from the American College of Rheumatology, Group for Research and Assessment of Psoriasis and Psoriatic Arthritis, European Alliance of Associations for Rheumatology, and Pan American League of Associations for Rheumatology, identifying similarities, differences, and opportunities for global harmonization with regional adaptation. METHODS: Narrative comparative review of guideline documents published between 2018 and 2024 by major rheumatology societies was performed. Data on methodology, panel composition, treatment domains, pharmacologic recommendations, and update strategies were extracted and synthesized. RESULTS: Guidelines share core principles, including domain-based approaches, treat-to-target strategies, and the use of conventional synthetic disease-modifying antirheumatic drugs and biologics. Differences arise from methodological frameworks (eg, GRADE [Grading of Recommendations Assessment, Development, and Evaluation], domain-based, adolopment), stakeholder composition, and explicit consideration of regional drug access. CONCLUSION: A hybrid framework combining global core recommendations with modular regional adaptations may optimize resource use, improve guideline sustainability, and maintain local relevance. Living systematic reviews and artificial intelligence could support more timely updates.
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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 | 0.312 | 0.450 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.007 | 0.009 |
| 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".