Radiologic approach to axial spondyloarthritis: where are we now and where are we heading?
Bibliographic record
Abstract
Current emphasis on diagnosing axial spondyloarthritis (axSpA) in early stage enforced the search for sensitive and specific diagnostic algorithms with the use of imaging methods. The aim of this review was to summarise current recommendations concerning the use of imaging techniques in diagnostics and monitoring of axSpA as well as to outline possible future directions of the development in this field. MEDLINE database was searched between March and April 2018. In the first phase, such keywords were applied: 'ASAS', 'EULAR', 'ASAS-EULAR', 'ASAS/OMERACT', 'axial spondyloarthritis', while in the second step: 'axial spondyloarthritis', 'ankylosing spondylitis', 'magnetic resonance imaging', 'computed tomography', and 'radiography', 'imaging'. An up-to-date summary of European League Against Rheumatism (EULAR) recommendations enriched with recent updates of Assessment of Spondyloarthritis International Society (ASAS) diagnostic criteria regarding imaging in axSpA course was created. Moreover, we outlined the role of new in this field, promising imaging techniques, such as diffusion-weighted imaging and dynamic contrast-enhanced sequences in magnetic resonance imaging (MRI) or low-dose computed tomography (CT). As precise monitoring of axSpA activity is vital, we reviewed the most precise methods: semiquantitative scores (e.g., Spondyloarthritis Research Consortium of Canada scores or CT Syndesmophyte Score) and quantitative analysis of MRI-based apparent diffusion coefficient or perfusion maps and enhancement curves. According to EULAR and ASAS recommendations, radiography and MRI still remain basic methods of axSpA diagnostics and monitoring. However, the knowledge of state-of-the-art international guidelines combined with the awareness of emerging imaging methods is the key to effective management of 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".