Bibliographic record
Abstract
176 Objectives: Atherosclerosis is an inflammatory disease, and FDG PET can identify and quantify inflammation within atherosclerotic plaque(1-3). Calcification is a late stage of atherosclerosis which may have a stabilizing effect on the plaque. What is not known is the relationship between atherosclerotic plaque inflammation across different arterial territories. We examined this relationship using PET/CT imaging. Methods: 43 asymptomatic patients with vascular disease underwent PET/CT imaging on a GE Discovery scanner. Aortic and carotid images were acquired 90 mins after 10 mCi FDG injection with CT used for coregistration and calcium scoring. To estimate FDG uptake into plaque, mean standardized uptake values (SUV) were calculated using ROI applied to the PET/CT images and corrected for blood FDG activity. Results: Mean age was 64 years. Mean SUV (standard deviation) values for each territory were as follows: ascending aorta 1.33 (0.42), arch 1.27 (0.41), descending aorta 1.18 (0.39), abdominal aorta 1.23 (0.39) and carotid 1.54 (0.25). Inflammation in one arterial territory was significantly correlated with inflammation in other vascular beds. This was true for all arteries except between the descending aorta with the carotid artery (Table – all values p<0.05 except in bold). Calcification and FDG uptake rarely overlapped, with a negative correlation noted in the ascending aorta between inflammation and calcification (r=-0.38, p<0.05). Conclusions: The systemic nature of atherosclerotic plaque inflammation is supported by the strong correlations between FDG uptake measured across multiple arterial territories. In the ascending aorta, the negative association between inflammation and calcification implies these may represent different stages of atherosclerotic disease.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".