The role of magnetic resonance imaging in monitoring patients with axial spondyloarthritis
Bibliographic record
Abstract
Introduction: Axial spondyloarthritis (axSpA) comprises a group of chronic inflammatory joint diseases. Modern therapies enable the rapid achievement of low disease activity or even remission. Therefore, assessing disease activity is now crucial for making the best possible therapeutic decisions. In addition to standard clinical indices used to evaluate disease activity, magnetic resonance imaging (MRI) is increasingly employed to assess inflammation. Material and methods: The study included patients with axSpA who had a Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) score ≥ 4 and a Spondyloarthritis Research Consortium of Canada (SPARCC) score ≥ 2. The MRI examinations of the sacroiliac joints were performed at the beginning and the end of the study to evaluate disease activity. The study lasted 3 months, during which patients were treated with certolizumab pegol. Results: The study included 31 patients with axSpA (11 females, 20 males). The mean age of the patients was 36.7 years (SD 9.7), and the mean disease duration from the onset of the first symptoms was 7.4 years (SD 1.9). At the start of therapy, all patients had active disease, as determined by clinical assessment (BASDAI ≥ 4 and Ankylosing Spondylitis Disease Activity Score [ASDAS] > 2.1) and MRI evaluation (SPARCC ≥ 2). The percentage of patients with active disease after 3 months of therapy was 26% (BASDAI), 19% (ASDAS), and 97% (SPARCC). Significant clinical improvement as a result of the therapy was observed in 81% (ΔBASDAI ≥ 50%), 97% (ΔASDAS ≥ 1.1), and 87% (ΔSPARCC ≥ 2.5) of patients. Conclusions: Magnetic resonance imaging provides a perspective on disease activity that complements traditionally used clinical indices. It does not replace these indices but rather offers additional insights during both the diagnostic process and the monitoring of therapy efficacy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".