Pan American League of Associations for Rheumatology Recommendations for the Treatment of Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: Psoriatic arthritis (PsA) is chronic disease that compromises multiple domains and might be associated with progressive joint damage, increased mortality, functional limitation, and considerably impaired quality of life. Our objective was to generate evidence-based recommendations on the management of PsA in Pan American League of Associations for Rheumatology (PANLAR) countries. METHODS: We used the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE)-ADOLOPMENT approach to adapt the 2019 recommendations of the European Alliance of Associations for Rheumatology. A working group consisting of rheumatologists from various countries in Latin America identified relevant topics for the treatment of PsA in the region. The methodology team updated the evidence and synthesized the information used to generate the final recommendations. These were then discussed and defined by a panel of 31 rheumatologists from 15 countries. RESULTS: Theses guidelines report 15 recommendations addressing therapeutic targets, use of antiinflammatory agents and corticosteroids, treatment with disease-modifying antirheumatic drugs (conventional synthetic, biologic, and targeted synthetic), therapeutic failure, optimization of biologic therapy, nonpharmacological interventions, assessment tools, and follow-up of patients with PsA. CONCLUSION: Here we present a set of recommendations to guide decision making in the treatment of PsA in Latin America, based on the best evidence available, considering resources, medical expertise, and the patient's values and preferences. The successful implementation of these recommendations should be based on clinical practice conditions, healthcare settings in each country, and a tailored evaluation of patients.
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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 | 0.020 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".