Emerging Concept of Intracranial Arterial Diseases: The Role of High Resolution Vessel Wall MRI
Bibliographic record
Abstract
Intracranial arterial disease (ICAD) is a heterogeneous condition characterized by distinct pathologies, including atherosclerosis. Advances in magnetic resonance technology have enabled the visualization of intracranial arteries using high-resolution vessel wall imaging (HR-VWI). This review summarizes the anatomical, embryological, and histological differences between the intracranial and extracranial arteries. Next, we review the heterogeneous pathophysiology of ICAD, including atherosclerosis, moyamoya or RNF213 spectrum disease, intracranial dissection, and vasculitis. We also discuss how advances in HR-VWI can be used to differentiate ICAD etiologies. We emphasize that one should consider clinical presentation and timing of imaging in the absence of pathology-radiology correlation data. Future research should focus on understanding the temporal profile of HR-VWI findings and developing quantitative interpretative approaches to improve the decision-making and management of ICAD.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".