Intracranial Hemodynamics Is Altered by Carotid Artery Disease and After Endarterectomy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Carotid endarterectomy (CEA) has become a routine procedure to treat symptomatic carotid artery disease and reduce the risk of recurrent cerebral ischemic events. The purpose of this study was to use an arterial spin labeling dynamic magnetic resonance angiography technique to characterize intracranial hemodynamics before and after CEA. METHODS: Thirty-seven carotid artery disease patients participated in this study, of whom 24 underwent magnetic resonance imaging before and after CEA. Seventeen control subjects spanning 5 decades underwent magnetic resonance imaging to assess age-related changes. Hemodynamic metrics (that is, relative time to peak and amplitude) were calculated with a γ-variate model. Linear regression was used to relate carotid artery disease burden to downstream hemodynamics in the circle of Willis. RESULTS: Relative time to peak increased with age in controls (P<0.020). For patients, relative time to peak was positively correlated with percent stenosis (P<0.050), independent of age. At 1 day after CEA, the middle cerebral artery ipsilateral to the CEA showed significant dynamic magnetic resonance angiography changes: relative time to peak decreased (P<0.017) and the flow amplitude increased (P<0.009). No pre- versus post-CEA changes were significant in the contralateral middle cerebral artery or posterior segments. CONCLUSIONS: This noninvasive, arterial spin labeling-based method produced time-resolved images that were used to characterize intracranial arterial flow associated with aging, extracranial carotid artery disease, and CEA. Results demonstrate that the technique has the sensitivity to detect hemodynamic changes after CEA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".