[Effects of carotid endarterectomy on cognitive function in patients with carotid stenosis].
Bibliographic record
Abstract
OBJECTIVE: To investigate the theraputic effects of carotid endarterectomy (CEA) on cognitive function in patients with carotid stenosis (CAS) and congnitive impairment. METHODS: CEA was performed on 38 patients with CAS from December 2011 to July 2013. There were 26 male and 12 female patients, with an average age of (70 ± 7) years. Patients was underwent neuropsychological examinations (NPEs) including Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) at 1 week before and 6 weeks after treatment. Cerebral perfusion was assessed with MR perfusion-weighted imaging and diffusion-weighted inmaging at 1 week before and 6 weeks after treatment. RESULTS: All of the 38 patients completed NPEs and MRI at baseline and 6 weeks after CEA. After therapy, the parameters and the extents of abnormal perfusion was improved, including the decrease of time to peak (29 ± 9 vs. 23 ± 4), relative mean transit time (22 ± 8 vs. 14 ± 6), arrive time (21 ± 8 vs. 15 ± 4) and relative cerebral blood volume (11.6 ± 3.5 vs. 7.5 ± 3.2) (t = 1.31 to 5.24, all P < 0.05). Significant improvement in MoCA (20.4 ± 1.5 vs. 22.0 ± 1.6, t = -4.25, P = 0.000) but MMSE (26.16 ± 1.35 vs. 26.47 ± 1.52, t = -0.96, P = 0.341) was observed. CEA significantly improved the assessment of visuospacial/constructive abilities (2.4 ± 0.9 vs. 2.8 ± 0.7), naming (2.0 ± 0.7 vs. 2.3 ± 0.6), abstraction (1.2 ± 0.7 vs. 1.6 ± 0.6) and attention (2.3 ± 0.6 vs. 2.6 ± 0.5) (t = 0.015 to 0.029, P = 0.015). CONCLUSION: CEA can improve the cognitive function of patients with carotid stenosis as well as the cerebral perfusion of patients and has therapeutic effects on vascular mild cognitive impairment.
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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 | 0.000 |
| 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".