International league of associations for rheumatology recommendations for the management of psoriatic arthritis in resource-poor settings
Bibliographic record
Abstract
BACKGROUND: Psoriatic arthritis (PsA) is a challenging heterogeneous disease. The European League Against Rheumatism (EULAR) and the Group for Research and Assessment of Psoriasis and PsA (GRAPPA) last published their respective recommendations for the management of PsA in 2015. However, these guidelines are primarily based on studies conducted in resource replete countries and may not be applicable in countries in the Americas (except Canada and USA) and Africa. We sought to adapt the existing recommendations for these regions under the auspices of the International League of Associations for Rheumatology (ILAR). PROCESS: The ADAPTE Collaboration (2009) process for guideline adaptation was followed to adapt the EULAR and GRAPPA PsA treatment recommendations for the Americas and Africa. The process was conducted in three recommended phases: set-up phase; adaptation phase (defining health questions, assessing source recommendations, drafting report), and finalization phase (external review, aftercare planning, and final production). RESULT: ILAR recommendations have been derived principally by adapting the GRAPPA recommendations, additionally, EULAR recommendations where appropriate and supplemented by expert opinion and literature from these regions. A paucity of data relevant to resource-poor settings was found in PsA management literature. CONCLUSION: The ILAR Treatment Recommendations for PsA intends to serve as reference for the management of PsA in the Americas and Africa. This paper illustrates the experience of an international working group in adapting existing recommendations to a resource-poor setting. It highlights the need to conduct research on the management of PsA in these regions as data are currently lacking.Key Points• The paper presents adapted recommendations for the management of psoriatic arthritis in resource-poor settings.• The ADAPTE process was used to adapt existing GRAPPA and EULAR recommendations by collaboration with practicing clinicians from the Americas and Africa.• The evidence from resource-poor settings to answer clinically relevant questions was scant or non-existent; hence, a research agenda is proposed.
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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 | 0.043 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".