Combined modality PET/MR for the detection of severe large vessel vasculitis
Bibliographic record
Abstract
BACKGROUND: Large vessel vasculitis (LVV) can be characterized based on symptom severity, and this characterization helps clinicians decide upon treatment approach. Our aim was to compare the imaging findings of combined modality positron emission tomography/magnetic resonance (PET/MR) and inflammatory markers between severe and non-severe LVV. A retrospective query was performed to identify all patients with LVV who underwent PET/MR at our institution between January 2015 and January 2021. RESULTS: Eleven patients (nine females; age 62.2 ± 16.4 years) underwent 15 PET/MR scans. Positivity was defined by findings indicative of active LVV on each modality: PET positive if vessel metabolic activity > liver metabolic activity; MR positive if wall thickening or contrast enhancement. When positive PET or positive MR findings were considered a positive scan, LVV patients with severe disease (n = 9 scans) showed a higher number of positive scans (n = 9) compared to the number of positive scans in non-severe patients (n = 3) (p < 0.05). The sensitivity and specificity for the detection of severe LVV were 1.00 and 0.50, respectively. When only the presence of both positive PET and positive MR findings were considered a positive scan, inflammatory marker levels were not significantly different between severe and non-severe LVV groups (severe: erythrocyte sedimentation rate (ESR) = 9.8 ± 10.6 mm/h; C-reactive protein (CRP) = 0.6 ± 0.4 mg/dL) (non-severe: ESR = 14.3 ± 22.4 mm/h; CRP = 0.5 ± 0.6 mg/dL). Blood- and liver-normalized maximum standardized uptake values were not significantly different between severe and non-severe patients (1.4 ± 0.3 vs 1.5 ± 0.4; 1.1 ± 0.4 vs 1.0 ± 0.3, respectively). CONCLUSIONS: F-fluorodeoxyglucose PET/MR may be able to supplement clinical symptoms-based LVV classification decisions and may be helpful when clinical symptoms overlap with other disease processes.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".