Magnetic resonance imaging in vascular biology☆
Bibliographic record
Abstract
Symptoms are only the tip of the iceberg in atherosclerotic disease. Beneath the surface are multiple patho-physiological processes taking place in and around the vessel wall. The increasing knowledge in the field of vascular biology also reveals new imaging targets as biological markers of the disease. Promising targets particularly relate to the early detection of subjects at risk and monitoring of therapeutical efforts. Among other imaging modalities magnetic resonance imaging (MRI) is an emerging tool with strong potential and a favourable safety profile. This article summarizes the different approaches of imaging various facets of atherosclerotic disease by MRI. In particular, endothelial function, arterial stiffness, vessel remodeling, angiogenesis inside the vessel wall, vessel stenosis and plaque characterization are addressed. As such MRI is a very versatile diagnostic tool for vascular biology research with high diagnostic accuracy and reproducibility of its results. Moreover, MRI allows for comprehensive studies, applying several techniques within one exam.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".