Temporal changes in symptomatic intracranial arterial disease: a longitudinal high-resolution vessel wall imaging study
Bibliographic record
Abstract
Introduction The temporal dynamics of the vessel wall in intracranial arterial disease (ICAD) may differ depending on the etiology. We investigated temporal changes in narrowed intracranial arteries after ischemic stroke using serial high-resolution vessel wall imaging (HR-VWI). Methods We retrospectively recruited patients with ICAD-related ischemic stroke who underwent two or more HR-VWI scans. The lumen area (LA), total vessel area (TVA), and enhancing area (EA) of the narrowest part of the culprit lesion were manually segmented. Degree of stenosis was estimated as [1-LA/TVA] × 100(%), the enhancing proportion as EA/TVA × 100(%), and enhancement ratio as (T1GDlesion/T1GDref)/(T1lesion/T1ref). Linear mixed models were used to investigate temporal changes in these parameters and whether such changes differed by etiologies. Results Of a total of 208 patients, ICAD-related stroke was caused by atherosclerosis (69%), arterial dissection (24%), vasculitis (3%), moyamoya disease (1%), and other (2%). The median follow-up was 319 [IQR, 125–409] days. HR-VWI imaging parameters, namely, degree of stenosis, enhancing proportion, and enhancement ratio showed a trend to decrease over time. Patients with intracranial dissection as a cause of intracranial narrowing showed a faster reduction in degree of stenosis and enhancing proportion vs. when such narrowing was identified as due to atherosclerosis (β [95% CI], −0.59%[−0.80% ~ −0.38%] and −0.81%[−1.23% ~ −0.39%], respectively, both p < 0.01). The enhancement ratio did not change over time in dissection, while it decreased in atherosclerosis (−0.01 [−0.02 ~ 0], p = 0.04). Conclusion Intracranial vessel narrowing in patients with ischemic stroke changes over time with different stroke etiologies having their own unique temporal patterns.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".