IMPACT OF CAROTID ENDARTERECTOMY ON COGNITIVE PERFORMANCE AND DEPRESSIVE SYMPTOMS
Bibliographic record
Abstract
OBJECTIVES: This study aims to evaluate the impact of carotid endarterectomy (CEA) on cognitive performance in patients with severe carotid disease and depressive symptoms, and to explore the possible associations between certain demographics, clinical characteristics, and cognitive function and depression.MATERIALS AND METHODS: The study included 48 patients, who were referred for endarterectomy. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) scale, while depressive symptoms were assessed using the patient health questionnaire(PHQ-9) scale. An assessment of cognitive and depressive symptoms was performed 1–3 days before surgery, and then six months after.RESULTS: A paired sample t-test found that the difference in the mean MoCA score between the before (=23.37; SD ± 3.27) and the after (=24.69; SD ± 3.68) surgery results was 1.32 (95% CI = 0.48 – 2.16; p= 0.003; Cohen’s d value = 0.95). A paired sample t-test showed that a decrease in mean PHQ-9 score of > 10 for patients six months after CEA (7.5±4.6) was statistically significant (p= 0.019; Cohen’s d value = 1.32) compared with the PHQ-9 scores at baseline (12.6 ± 2.8).CONCLUSION: Carotid artery endarterectomy seems to have beneficial effects on the course of cognitive impairment and depressive symptoms in patients with severe carotid artery stenosis. Demographic, clinical characteristics (age, gender, comorbidities, previous stroke) did not have impact on course of cognitive and depressive symptoms. A limitation in our study was that the number of patients was relatively small, therefore we intend to perform further study with larger case volume to estimate the impact of carotid artery endarterectomy on cognitive functions and depressive 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 | 0.000 | 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".