18Fluoride-based molecular imaging of coronary atherosclerosis in HIV infected patients
Bibliographic record
Abstract
Background and aims Molecular imaging with 18 Fluorodeoxyglucose (FDG) and 18 F-sodium-fluoride (NaF) captures arterial inflammation and micro-calcification and can reveal potentially unstable atherosclerotic plaques. Methods We performed FDG and NaF PET/CT imaging in two clinically similar cohorts of patients living with HIV (PLWH) with no symptomatic cardiovascular disease. The prevalence and intensity of coronary artery uptake of each tracer, measured as target-to-background ratio (TBR), were assessed in patients at low and high cardiovascular risk. Results Ninety-three PLWH were submitted to PET/CT imaging with FDG (N = 43) and NaF (N = 50); 42% were at low and 58% at high cardiovascular risk. The intensity of uptake and multivessel coronary artery uptake were significantly higher with NaF than FDG both in low and high-risk patients. When each 18 F-tracer was tested in low and high-risk patients, an equal proportion of subjects showed no vessel, single and multivessel NaF uptake; the same was true for no and single vessel uptake of FDG (no multivessel FDG uptake was noted). Waist circumference, CRP, D-dimer, HIV duration and treatment with nucleoside reverse transcriptase inhibitors were associated with high NaF uptake in univariable analyses; D-dimer remained significant in multivariable analyses (OR = 1.05; p= 0.02). There were no significant associations with FDG uptake. Conclusions The prevalence of coronary artery uptake was higher with NaF compared to FDG both in high and low risk patients, hence microcalcification imaging may be a more sensitive tool to detect coronary atherosclerosis than inflammation imaging. However, the uptake of each 18 Fluoride tracer was similar between low and high-risk subjects, and this underscores the discordance between clinical and imaging based risk assessment. Future investigation should address the prognostic significance of NaF coronary artery uptake.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".