Establishing Centers of Excellence for Psoriatic Disease in Latin America: Consensus Recommendations From the REAL-PANLAR Group
Bibliographic record
Abstract
OBJECTIVE: Psoriatic disease (PsD) is a chronic, multisystem, inflammatory condition characterized by heterogeneous manifestations, including peripheral arthritis, axial involvement, enthesitis, and cutaneous and nail psoriasis. The condition has significant physical, emotional, and psychosocial effects on patients. In Latin America, healthcare disparities exacerbate delays in diagnosis and treatment, increasing the burden of PsD and associated comorbidities. This study aimed to establish regionally adapted criteria for Centers of Excellence (COEs) to optimize PsD care. METHODS: A panel of 18 experts in rheumatology and dermatology from 12 Latin American countries developed COE criteria using the Delphi methodology. A narrative literature review informed the process, and criteria were evaluated using a Likert scale. Consensus was defined as ≥ 70% agreement, and an in-person meeting refined unresolved items. The criteria were categorized into structure, process, and outcomes, based on the Donabedian quality evaluation model. RESULTS: Two types of COEs were defined: optimal and model. Optimal COEs require a multidisciplinary team including rheumatologists, dermatologists, nurses, and psychologists. Model COEs expand this team to include gastroenterologists, ophthalmologists, physiatrists, among other specialists. Structural criteria emphasized infrastructure and electronic systems for data management. Process criteria included patient-centered education, multidisciplinary consultations, and psychosocial support. Outcomes focused on standardized clinimetric tools (eg, Psoriasis Area and Severity Index, Disease Activity Index for Psoriatic Arthritis) and the treat-to-target strategy. Approval ratings ranged from 80% to 100%. CONCLUSION: The consensus establishes a framework for COEs in PsD care in Latin America, addressing structural, process, and outcome criteria to improve clinical outcomes, patient satisfaction, and healthcare system sustainability. These standards provide a roadmap for enhancing PsD management in resource-limited settings.
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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 | 0.173 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.011 | 0.017 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".