Functional MR Imaging in Patients with Carotid Artery Stenosis before and after Revascularization
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Significant extracranial stenosis of the ICA is a known risk factor for future stroke and it has been shown that revascularization reduces the risk of future stroke. We applied BOLD fMRI in patients with carotid artery stenosis before and after CEA. Our purpose was to determine whether fMRI is able to demonstrate impaired CVR and to identify patient parameters that are associated with postoperative changes of cerebral hemodynamics. MATERIALS AND METHODS: Nineteen consecutive patients with symptomatic (n = 13) and asymptomatic (n = 6) stenosis of the ICA were prospectively recruited (male/female ratio = 16:3; age, 69 ± 8,1 years). fMRI using a simple bilateral motor task was performed immediately before and after CEA. RESULTS: Mean BOLD MSC was significantly increased postoperatively (MSC, 0.13 ± 0.66; P = 0.0002). Patients with a stenosis of <80% demonstrated an increase in MSC (MSC, 0.32 ± 0.59; P ≤ .0001). Patients with previous ischemic stroke showed a larger MSC than patients with TIAs (stroke: MSC, 0.55 ± 0.65; P ≤ .0001; TIA: MSC, 0.05 ± 0.26; P = 0.054). Patients older than 70 years had a significantly larger MSC following surgery (≤70 years: MSC, -0.01 ± 0.39; P = .429; >70 years: MSC, 0.29 ± 0.48; P ≤ .0001). CONCLUSIONS: BOLD fMRI can demonstrate changes in cerebral hemodynamics before and after CEA, indicative of an ameliorated CVR. This response is dependent on the age of the patient, the degree of preoperative stenosis, and the patient's symptoms.
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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 | 0.001 | 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".