Carotid Stenosis and Cognitive Function: An Update on Therapeutic Interventions
Bibliographic record
Abstract
Carotid stenosis (CS) is closely associated with cognitive decline, primarily affecting memory, attention, and executive function. This relationship is explained by mechanisms such as chronic cerebral hypoperfusion and asymptomatic microembolism. Interventions like carotid endarterectomy (CEA) and carotid artery stenting (CAS) have demonstrated potential benefits in restoring cerebral perfusion; however, outcomes are variable, particularly in domains such as executive function. These differences may be attributed to patient characteristics, the degree of stenosis, and the technique employed. Revascularization is more commonly associated with the stabilization of cognitive decline rather than the active improvement of cognitive function. CEA has shown superiority over CAS in promoting recovery of cerebral connectivity and hemodynamic stability. Improvements have been documented using instruments such as the Montreal Cognitive Assessment (MoCA), especially in patients with baseline cognitive impairment. Complications such as postoperative cognitive dysfunction (POCD) and hyperperfusion syndrome underscore the importance of appropriate patient selection, taking into account factors such as advanced age, hypertension, and bilateral stenosis. Biomarkers such as the neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios are associated with a higher risk of postoperative cognitive deterioration. Imaging modalities, including functional magnetic resonance imaging, support evidence of functional recovery following CEA. Questions remain regarding the long-term benefits, optimal selection criteria, and predictive value of biomarkers, all of which represent key areas for future research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".