Features of Cognitive and Emotional Disorders in the Early Recovery Period of Ischemic Stroke
Bibliographic record
Abstract
Presence and severity of cognitive and emotional disorders were studied in patients in the early recovery period of hemispheric ischemic stroke. The mini–mental state examination (MMSE), the Montreal cognitive assessment scale (MoCA) and the Fullerton Advanced Balance FAB scale were used for the the diagnosis of cognitive impairment. Depressive and anxiety disorders were assessed with the help of the Zung Self-Rating Depression Scale, the Hospital Anxiety and Depression Scale (HADS), and the Spielberger-Khanin Questionnaire of Reactive and Personal Anxiety. Cognitive impairment (CI) was found in 87.7% of patients, among which light (24.6%) and moderate CI (36.9%) predominated. The average MMSE score in patients was 24.15 ± 0.45 compared to 29.16 ± 0.38 in the control group. In the main group of patients the average MoCA score was 18.73 ± 0.65, whereas in the control group it was 28.05 ± 0.24. Depressive syndrome was found in the emotional status of 46.2% of patients, and anxiety syndrome was diagnosed in 40.0% of patients. In the structure of emotional disorders predominantly mild depressive syndrome (36.9%) and mild anxiety syndrome (35.4%) were found. The combination of both anxiety and depressive syndromes was observed in 18 (27.7%) patients. The average HADS anxiety score was 9.64±0.45, the HADS depression score was 11.48±0.36 versus 4.32±0.29 and 3.98±0.35 points in the control group, respectively. The MoCA scale showed itself as more sensitive and allowed to diagnose cognitive impairment in 87.7% of patients while MMSE scale detected CI in only 73.8% (p<0.05). Therefore, the use of the Montreal scale will help achieve more accurate and early diagnosis of cognitive dysfunction in the post-stroke period.
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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) | 0.001 | 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".