The Use of Magnetic Resonance Imaging in Axial Spondyloarthritis: Time to Bridge the Gap Between Radiologists and Rheumatologists
Bibliographic record
Abstract
OBJECTIVE: Magnetic resonance imaging (MRI) is involved in the assessment of axial spondyloarthritis (axSpA); however, anecdotal evidence suggests diverse practice among radiologists. The objective of this study was to describe current practice in the use of MRI for assessment of axSpA by UK radiologists. METHODS: Six hundred ninety-nine UK radiologists were invited to complete an online survey. Availability of MR scanners, familiarity with axSpA disease-specific lesions, and MRI protocols and definitions of positive sacroiliac joint (SIJ) or spinal MRI were assessed. RESULTS: Two-hundred sixty-nine radiologists (38%) from 131/180 (73%) acute UK National Health Service trusts/health boards responded. MRI waiting times < 2 months were reported by 90% of radiologists. Twenty-nine radiologists (11%) used contrast as standard, 256 (91%) used T1 and short-tau inversion recovery, and 172 (64%) also used T2 sequences. Five percent scanned only SIJ, 33% scanned SIJ and lumbar spine, 29% scanned SIJ and thoracolumbar spine, and 30% scanned SIJ and the whole spine. Mean scan time was 34 min. Eighteen percent did not use the subchondral bone marrow edema of the SIJ to help diagnose axSpA and 18% did not use the inflammatory vertebral corner lesions to assist diagnosis. Awareness of axSpA was reported by 75% of radiologists, and awareness of definitions for positive MRI of SIJ and spine by 31% and 25%, respectively. CONCLUSION: These data highlight the need for better rheumatology-radiology collaboration on the identification of diagnostic axSpA MRI lesions and support the need for a consensus on the most appropriate MRI protocols for the assessment 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 | 0.007 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".