The Effects of Carotid Revascularization on 1-Year Cognitive Performance in Patients With Carotid Artery Stenosis
Bibliographic record
Abstract
PURPOSE: The impact of carotid revascularization on cognitive function for patients with severe carotid artery stenosis remains uncertain. This study is aimed to investigate the 1-year neurocognitive outcomes of patients who accept carotid revascularization and identify the risk factors associated with postoperative cognitive decline. METHODS: From April 2019 to April 2021, patients with ≥70% carotid artery stenosis who were treated with carotid endarterectomy (CEA) or carotid artery stenting (CAS) were recruited for this study. The Montreal Cognitive Assessment (MoCA) instrument was used to evaluate cognitive function preoperatively and at 3, 6, and 12 months postoperatively. Logistic regression analysis was built to identify potential risk factors for postoperative long-term cognitive decline. RESULTS: A total of 89 patients who met the criteria were enrolled and completed 1-year follow-up. At 3, 6, and 12 months after carotid revascularization, the total MoCA score, attention, language fluency, and delayed recall score were significantly improved compared with the baseline scores (p<0.05). At 12 months, there was also a significant improvement in cube copying compared with baseline (p=0.034). Logistic regression analysis showed that the advancing age, left side, and symptomatic carotid artery stenosis were independent risk factors for cognitive deterioration at 12 months after surgery. CONCLUSIONS: Overall, carotid revascularization has a beneficial effect on cognition function in patients with severe carotid artery stenosis, while advancing age, left side, and symptomatic carotid artery stenosis were significantly related to a decreased cognitive score after carotid revascularization.Clinical ImpactThis study focused on the changes in cognitive function within 1 year after carotid revascularization in patients with severe carotid stenosis. Of course, carotid revascularization can improve the cognition function in these patients. On the other hand, we found the advancing age, left side and symptomatic carotid artery stenosis were significantly associated with decreased cognitive scores at 1 year after carotid revascularization, which suggests that clinicians may need to be aware of patients with these characteristics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".