Clinical information on imaging referrals for suspected or known axial spondyloarthritis: recommendations from the Assessment of Spondyloarthritis International Society (ASAS)
Bibliographic record
Abstract
OBJECTIVES: This study aims to establish expert consensus recommendations for clinical information on imaging requests in suspected/known axial spondyloarthritis (axSpA), focusing on enhancing diagnostic clarity and patient care through guidelines. MATERIALS AND METHODS: A specialised task force was formed, comprising 7 radiologists, 11 rheumatologists from the Assessment of Spondyloarthritis International Society (ASAS) and a patient representative. Using the Delphi method, two rounds of surveys were conducted among ASAS members. These surveys aimed to identify critical elements for imaging referrals and to refine these elements for practical application. The task force deliberated on the survey outcomes and proposed a set of recommendations, which were then presented to the ASAS community for a decisive vote. RESULTS: The collaborative effort resulted in a set of six detailed recommendations for clinicians involved in requesting imaging for patients with suspected or known axSpA. These recommendations cover crucial areas, including clinical features indicative of axSpA, clinical features, mechanical factors, past imaging data, potential contraindications for specific imaging modalities or contrast media and detailed reasons for the examination, including differential diagnoses. Garnering support from 73% of voting ASAS members, these recommendations represent a consensus on optimising imaging request protocols in axSpA. CONCLUSION: The ASAS recommendations offer comprehensive guidance for rheumatologists in requesting imaging for axSpA, aiming to standardise requesting practices. By improving the precision and relevance of imaging requests, these guidelines should enhance the clinical impact of radiology reports, facilitate accurate diagnosis and consequently improve the management of patients with axSpA.
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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 | 0.115 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".