A Personalized Methodology for Assessing Early Post-Stroke Cognitive Impairment
Bibliographic record
Abstract
The purpose of а study is to identify the main indicators of the individual profile of patients with early post-stroke cognitive impairment. The study included 200 patients diagnosed with ischemic stroke with cognitive decline. Medical history included an assessment of demographic parameters, cardiovascular risk factors, and comorbidities. The functional status of patients was assessed using various assessment tools: the Barthel Index, the Modified Rankin Scale, and the National Institutes of Health Stroke Scale. The patient’s cognitive and psycho-emotional profile was assessed using scales: the Montreal Cognitive Assessment Scale, the Informant Questionnaire on Cognitive Decline in the Elderly, the Modified Hachinski Ischemic Scale, the Hospital Anxiety and Depression Scale, the Apathy Evaluation Scale, the Multidimensional Fatigue Inventory-20, the Buss-Perry Aggression Questionnaire-24 and additional scales for assessing praxis, semantic aphasia, perception, and executive function. To objectively assess cognitive dysfunction, long-latency acoustic endogenous evoked potential parameters were assessed. The laboratory tests included the evaluation of the levels of cytokines. Neuroimaging parameters (stroke location, preexisting vascular and neurodegenerative disease) were assessed using magnetic resonance imaging (MRI). The patient profile with varying severity of cognitive impairment, pre-stroke cognitive decline, and lesion lateralization was determined by discriminant analysis of clinical and paraclinical parameters using ML algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".