Carotid Artery Stenting Intervention to Enhance Global Brain Blood Flow and Cognition in Carotid Artery Disease: Preliminary Findings from a Prospective Follow-Up MRI Study
Bibliographic record
Abstract
Background and Objectives: The benefit of carotid artery stenting (CAS) for stroke prevention has been established, but less is known about CAS’s effect on cognition. Here, we investigate (1) changes in the blood flow in both treated and non-treated carotid arteries, (2) associations between the severity of artery occlusion and CAS-induced flow change, and (3) whether the flow changes relate to cognitive improvement. Materials and Methods: We used quantitative flow magnetic resonance imaging to assess blood flow and computerized neurocognitive assessment to evaluate cognitive performance. Fourteen patients identified for CAS as part of their standard care participated in this study; ten completed the CAS procedure and the pre- and post-CAS assessments (age = 77.0 ± 5.6; 70% males). Results: An increased ipsilateral flow following CAS was seen in 70% of the participants, while 50% also showed an increase in the total flow. The participants with ≥90% stenosis showed the greatest flow changes, such that the post-CAS flow was 60% higher relative to pre-CAS (p < 0.050). Cognitive responses to the flow increase were variable: attention showed a positive association; in comparison, higher cognitive flexibility and memory were only seen when treated stenosis was below 80%. Conclusions: Our preliminary findings highlight the impact of CAS and the complex relationship between blood flow and cognitive changes post-CAS, warranting larger-scale studies with extended follow-up periods.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".