Predictors of the dynamics of changes in cognitive functions in patients 6 months after carotid endarterectomy
Bibliographic record
Abstract
Background. Carotid atherosclerosis is one of the urgent problems due to the high risk of developing ischemic stroke and cognitive impairment. The dynamics of clinical disorders in patients with carotid stenosis is determined by a complex of neurophysiological, angiological, tissue and biomolecular reactions, the characteristics of which can act as predictors of the course of the pathology.The aim of the work. To determine the neurophysiological parameters and predictors of cognitive dysfunction in patients who underwent carotid endarterectomy.Materials and methods. The study included 59 people with carotid atherosclerotic disease. All included patients underwent carotid endarterectomy. We assessed the degree of stenosis of the internal carotid artery and cognitive status using the FAB (Frontal Assessment Battery) scale and MoCA (Montreal Cognitive Assessment) Test and recorded electroencephalogram (EEG), P300 cognitive evoked potentials and heart rate variability in patients at various terms (before surgery, 6 months after the surgery). Patients were divided into groups based on the dynamics of cognitive tests using cluster analysis (k-means) with identification of elements included in the clusters: patients of cluster 1 had a “preserved” profile of cognitive status; patients of cluster 2 – moderate cognitive dysfunction.Results. Patients of cluster 1 had a higher power of beta oscillations in the frontal lead, a higher amplitude of the P3 component of the P300 potential, and a greater variability of R-R intervals in terms of the total indicator and high-frequency power. We proposed a model that allows us to classify patients into groups according to the dynamics of cognitive function scores. According to the data obtained, the most significant predictors of the dynamics of cognitive status were the initial characteristics of the EEG and the P300 cognitive evoked potential.Conclusions. We determined the clinical and neurophysiological correlates of cognitive dysfunction: an association with greater preservation of activating effects on the EEG, processes of recognition and decision-making in the associative zones of the cortex, and less pronounced activity of stress-implementing mechanisms. Indicators of EEG spectral analysis and characteristics of the P300 cognitive evoked potential are predictors of the cognitive status dynamics.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".