Bibliographic record
Abstract
Aim. To identify significant indicators of cognitive dysfunction based on discriminant analysis and to assess the influence of the course, nature and localization of ischemic stroke on the cognitive status of the patient.Materials and methods. We examined 290 patients diagnosed with ischemic stroke in the carotid artery area. Depending on presence of cognitive dysfunction according to the Montreal Cognitive Assessment Scale (MoSA) patients were divided into 2 groups: 240 patients with cognitive decline (≤25 point by MoCA) and 50 patients without it. In order to verify the markers, anamnestic characteristics were assessed, cognitive-functional indicators (according to the scales of the National Institutes of Health, MoCA, Bartel, Rankin, IQCODE questionnaire, additional scales to assess praxis, semantic aphasia, perception and executive function), data of neuroimaging studies. For statistical analysis machine learning algorithms and Python with its libraries (Pandas and SciPy) were implied.Results. The main neuropsychological indicators for patients with early post-stroke cognitive impairment were decline in the areas of perception, executive function, memory and semantic information processing, affective disturbances and physical fatigue. Relevant indicators identified during estimation of the instrumental and clinical examination results were severity of IS, left frontal and right parietal localisations of ischemia focus, presence of cortical atrophy and leukoaraiosis.Conclusion. Based on multi-factor analysis of clinical and paraclinical parameters using machine learning algorithms, the main markers of cognitive decline of early post-stroke impairments were identified. This will allow us to optimise the choice of neurocognitive rehabilitation strategies and to personalise the approach in the further management of the stroke patient.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".