Vessel wall MRI in giant cell arteritis: standardized protocol and scoring approach developed by an international working group
Bibliographic record
Abstract
OBJECTIVES: There are an increasing number of centres performing research on high-resolution vessel wall magnetic resonance imaging (VW-MRI) in GCA. However, harmonized approaches to VW-MRI in GCA are lacking and are essential to performing multicentre studies. Using a data-driven, consensus-based approach, an international expert group developed a standardized MRI protocol and scoring system to advance multi-centred research in cranial GCA. METHODS: A targeted literature review of VW-MRI in cranial GCA was conducted. A working group comprised of radiologists, rheumatologists and ophthalmologists with expertise in VW-MRI and GCA reviewed the results of the literature search, presented relevant data and images from their respective centres, and then reached consensus on recommendations related to key MRI structures, MRI sequences, scoring system and other important considerations. RESULTS: A total of 21 relevant articles were identified and reviewed. Based on published literature, structures to be evaluated on MRI were categorized based on anatomic location (extradural cranial, intradural cranial and orbits) and prioritization (core vs elective). Essential and elective sequences to comprehensively image cranial and orbital structures while minimizing scan time were determined along with scoring systems to grade contrast enhancement. CONCLUSION: This report describes a standardized approach to facilitate research of VW-MRI in cranial GCA that is the result of a multidisciplinary, international collaboration of experts in VW-MRI and/or GCA.
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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 | 0.065 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".