Longitudinal changes on cranial magnetic resonance imaging in relapsing giant cell arteritis
Bibliographic record
Abstract
OBJECTIVE: There is a need for better tools to monitor disease activity in giant cell arteritis (GCA). Prior studies demonstrated that vascular enhancement on cranial vessel wall magnetic resonance imaging (vw-MRI) decreases with treatment of GCA, but whether enhancement increases during relapse is not well known. This study examined changes on vw-MRI during relapse of cranial GCA. METHODS: Patients with active GCA acquired cranial vw-MRIs at enrollment and months 1, 6, and 12 and if suspected relapse occurred. Neuroradiologists graded vw-MRI enhancement for several structures. Changes in MRI scores were compared with clinically-determined disease activity and acute phase reactants (APR). RESULTS: Fourteen patients with GCA were included: 4 patients experienced a cranial or ocular relapse; 2 patients experienced a relapse with polymyalgia rheumatica (PMR) without cranial symptoms; and 8 patients were in sustained remission. All 4 patients who experienced cranial or ocular relapse had increased vw-MRI enhancement in at least one cranial structure. Two patients experiencing relapse of PMR had persistent but not increased enhancement, while 7 of 8 patients in sustained remission had decreased or normal enhancement on follow-up. Cranial structures that showed increased enhancement at relapse included the occipital artery, optic nerve sheath, and maxillary artery. APR levels remained normal in most relapses, likely impacted by use of tocilizumab. CONCLUSION: During relapse of cranial GCA, increased contrast enhancement of cranial structures is observed on vw-MRI even when APR levels remained normal. These data offer proof-of-concept that vw-MRI has potential for longitudinal disease monitoring of 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".