Magnetic resonance imaging and clinical outcome in patients with symptomatic carotid artery stenosis after carotid artery revascularization
Bibliographic record
Abstract
Introduction: About 30% of patients with carotid artery stenosis (CAS) develop dementia after a cerebral ischemic event (CIE), and 20-50% suffer from CIE recurrence during 6 months.Carotid artery revascularization (CAR) may prevent CIE recurrence, at the cost of new microembolic lesions (MES).The impact of CAR on cognitive function is debatable.Aim: To assess functional and cognitive outcome, cerebral flow on transcranial Doppler (TCD) and brain magnetic resonance imaging (MRI) in patients with symptomatic CAS referred for CAR.Material and methods: Twenty-two patients (aged 69.0 ±7.2 y.o., 15 male) with recent CIE (21.9 ±20.9 days to CAR) related to CAS of mean 89.8 ±3.9% lumen reduction were prospectively evaluated with TCD, diffusion and perfusion MRI, Montreal Cognitive Assessment (MoCA), Mini Mental State Examination (MMSE), modified Rankin Scale (mRS) and the National Institutes of Health Stroke Scale (NIHSS) 24 h before, at 24-48 h and 1 month following CAR.Results: New MES were found in 11 (50%) subjects following CAR.CAR resulted in a significant increase of cerebral flow velocity in the middle and anterior cerebral arteries (p < 0.002 and p = 0.003; respectively) and cerebral perfusion measured by time to peak (TTP) and mean transit time (MTT) (p = 0.0009 and p = 0.0002; respectively).Neurologic tests showed improvement in NIHSS (2.4 ±1.6 to 1.5 ±1.2, p = 0.003), mRS (from 1.3 ±0.9 to 0.7 ±0.9, p = 0.005), and MMSE (26.7 ±2.2 to 27.6 ±2.3, p = 0.019) at 1 month, while similar MoCA scores were observed before and 1 month after CAR (23.4 ±3.3 vs. 24.1 ±3.7, p = 0.136).Conclusions: Improvement of cerebral flow and perfusion and functional outcome, as well as at least no cognitive decline, is observed after CAR for symptomatic CAS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".