Establishing Centers of Excellence for Axial Spondyloarthritis in Latin America: Consensus Recommendations From the REAL-PANLAR Group
Bibliographic record
Abstract
OBJECTIVE: Axial spondyloarthritis (axSpA) is a chronic inflammatory condition primarily affecting the sacroiliac joints and spine, often complicated by extramusculoskeletal manifestations such as uveitis, psoriasis, and inflammatory bowel disease. Delayed diagnosis due to nonspecific symptoms, coupled with regional disparities in healthcare infrastructure in Latin America, exacerbates disease burden, emphasizing the need for specialized care. This project aimed to develop a regional consensus for establishing Centers of Excellence (COEs) in axSpA management. METHODS: A Delphi methodology was employed, involving 16 rheumatology experts from 12 Latin American countries. A structured process included a systematic literature review, questionnaire validation, and consensus building during a virtual and in-person meeting. Criteria were categorized into initial premises, structure, processes, and outcomes, guided by the Donabedian quality evaluation framework. RESULTS: The consensus established 3 COE classifications-standard, optimal, and model-defined by resource availability and care standards. Human resources criteria highlighted multidisciplinary teams, including rheumatologists, physiatrists, and dermatologists, with agreement rates ranging from 70.6% to 100%. Structural requirements, such as electronic health systems for traceability and continuous training, achieved consensus levels between 81.3% and 100%. Process-related criteria emphasized comprehensive care models, treat-to-target strategy implementation, and validated clinimetric tools (eg, Ankylosing Spondylitis Disease Activity Score, Bath Ankylosing Spondylitis Disease Activity Index), with approval ratings of 70.6% to 100%. CONCLUSION: This consensus establishes a scalable framework for COEs in axSpA in Latin America, balancing high-quality care standards with regional healthcare limitations.
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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 | 0.234 | 0.159 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.011 | 0.019 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".