Development of international consensus on a standardised image acquisition protocol for diagnostic evaluation of the sacroiliac joints by MRI: an ASAS–SPARTAN collaboration
Bibliographic record
Abstract
BACKGROUND: A range of sacroiliac joint (SIJ) MRI protocols are used in clinical practice but not all were specifically designed for diagnostic ascertainment. This can be confusing and no standard diagnostic SIJ MRI protocol is currently accepted worldwide. OBJECTIVE: To develop a standardised MRI image acquisition protocol (IAP) for diagnostic ascertainment of sacroiliitis. METHODS: 13 radiologist members of Assessment of SpondyloArthritis International Society (ASAS) and the SpondyloArthritis Research and Treatment Network (SPARTAN) plus two rheumatologists participated in a consensus exercise. A draft IAP was circulated with background information and online examples. Feedback on all issues was tabulated and recirculated. The remaining points of contention were resolved and the revised IAP was presented to the entire ASAS membership. RESULTS: A minimum four-sequence IAP is recommended for diagnostic ascertainment of sacroiliitis and its differential diagnoses meeting the following requirements. Three semicoronal sequences, parallel to the dorsal cortex of the S2 vertebral body, should include sequences sensitive for detection of (1) changes in fat signal and structural damage with T1-weighting; (2) active inflammation, being T2-weighted with fat suppression; (3) bone erosion optimally depicting the bone-cartilage interface of the articular surface and (4) a semiaxial sequence sensitive for detection of inflammation. The IAP was approved at the 2022 ASAS annual meeting with 91% of the membership in favour. CONCLUSION: A standardised IAP for SIJ MRI for diagnostic ascertainment of sacroiliitis is recommended and should be composed of at least four sequences that include imaging in two planes and optimally visualise inflammation, structural damage and the bone-cartilage interface.
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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 | 0.362 | 0.306 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.009 | 0.013 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".