Imaging in clinical trials of axial spondyloarthritis: what type of imaging should be used
Bibliographic record
Abstract
Axial spondyloarthritis (axSpA) is a chronic inflammatory condition predominantly affecting the sacroiliac joints and spine. Early and accurate diagnosis is crucial to prevent structural damage and improve patient outcomes. Imaging plays a pivotal role in axSpA diagnosis, monitoring, and clinical trials, offering insights into both inflammatory activity and structural progression. Conventional radiography has been foundational for detecting structural changes, such as syndesmophytes and erosions, but it is limited by poor sensitivity for early disease detection and significant interobserver variability. Advanced imaging modalities, such as magnetic resonance imaging (MRI) and low-dose computed tomography (ld-CT), have emerged as more sensitive tools. MRI excels in identifying active inflammation, particularly bone marrow edema, and is integral to early diagnosis and disease monitoring. ld-CT provides superior spatial resolution for detecting structural lesions while minimizing radiation exposure. However, challenges remain in achieving standardized imaging protocols and consistent scoring systems across clinical trials. Scoring systems like the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS), Spondyloarthritis Research Consortium of Canada (SPARCC) scores, and Berlin methods require rigorous calibration to ensure reliability. The purpose of this review is to explore the strengths and limitations as well as the use in clinical trials of the different imaging modalities and to offer guidance on selecting the most suitable imaging techniques for assessing both disease activity and structural progression in clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".