Imaging Biomarkers in Atherosclerosis Trials
Bibliographic record
Abstract
Advantages and Disadvantages Coronary angiography Coronary lumen 0.4 mm Yes Yes Two-dimensional silhouette of lumen only, associated with cardiovascular events, accepted surrogate for expanded indication IVUS Coronary arterial wall 0.08-0.10mm No Yes, catheter Tomographic, volumetric assessment of changes in plaque burden and vascular remodeling with excellent resolution Carotid ultrasound cIMT and plaque 0.15 mm No No Wide availability, wide clinical trial experience, low cost, technically demanding; further validation of 3D plaque assessment required Coronary CT Coronary lumen and wall 0.4 mm Yes No Rapid acquisition, limited image resolution for plaque burden, radiation exposure MRI Carotid lumen and wall 0.5-1.0mm No No Plaque characterization possible, no validation with clinical events PET Metabolic activity in carotids and aorta 4 mm Yes No Limited image resolution, limited ability to visualize coronary arteries, no validation with clinical events OCT Coronary arterial lumen and wall Excellent, 0.01 mm No Yes, catheter Best image resolution, limited depth of view Requires flushing of lumen NIRS Tissue spectral contrast (chemical assessment) 1 mm No Yes, catheter Link to plaque status not clear, images difficult to interpret, limited spatial registration NIR fluorescence Exogenous, molecular contrast 1 mm No Yes, catheter and injection of molecular compound Limited depth of view due to blood absorption, may require flushing; high potential for molecular imaging; depends on targeted compound approval for humans
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".