“Vascular inflammation and cardiovascular disease: review about the role of PET imaging”
Bibliographic record
Abstract
Abstract Inflammation characterizes all stages of atherothrombosis and provides a critical pathophysiological link between plaque formation and its acute rupture, leading to coronary occlusion and heart attack. In the last 20 years the possibility of quantifying the degree of inflammation of atherosclerotic plaques and, therefore, also of vascular inflammation aroused much interest. 18Fluoro-deoxy-glucose photon-emissions-tomography (18F-FDG-PET) is widely used in oncology for staging and searching metastases; in cardiology, the absorption of 18F-FDG into the arterial wall was observed for the first time incidentally in the aorta of patients undergoing PET imaging for cancer staging. PET/CT imaging with 18F-FDG and 18F-sodium fluoride (18F-NaF) has been shown to assess atherosclerotic disease in its molecular phase, when the process may still be reversible. This approach has several limitations in the clinical practice, due to lack of prospective data to justify their use routinely, but it’s desirable to develop further scientific evidence to confirm this technique to detect high-risk patients for cardiovascular events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".