Ultrasound in the Management of Patients With Psoriatic Arthritis: Systematic Literature Review and Novel Algorithms for Pragmatic Use
Bibliographic record
Abstract
OBJECTIVE: In 2015, the European Alliance of Associations for Rheumatology (EULAR) published recommendations for the use of imaging for the diagnosis and management of spondyloarthritis (SpA) in clinical practice. These recommendations included the use of ultrasound (US) in patients with psoriatic arthritis (PsA), but the management was not clearly distinguished from that of SpA. We aimed to systematically review the literature on the role of US for the management of PsA, and to propose pragmatic algorithms for its use in clinical practice. METHODS: A group of 10 rheumatologists, experienced in imaging and musculoskeletal US, met with the objectives of formulating key questions for a systematic literature review (SLR), appraising the available evidence, and then proposing algorithms on the application of US in suspected or established PsA, based on both the literature and experts' opinions following a Delphi process. RESULTS: The SLR included 120 articles, most of which focused on the diagnostic process. The elevated number of articles retrieved suggests the interest of rheumatologists in using US in the management of PsA. After a consensual discussion on literature data and expert opinion, the following 3 algorithms were developed to be used in practical situations: suspicion of PsA, management of PsA with good clinical response, and management of PsA with insufficient clinical response. CONCLUSION: The SLR showed interest by rheumatologists in using US to objectively evaluate PsA for diagnosis and management. We propose 3 practical algorithms to guide its use in the clinical management of patients, from diagnosis to the assessment of treatment response. Further studies are needed to define remission and to assess the ability of US to predict disease severity.
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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 | 0.103 | 0.271 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.035 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".