THE INFLUENCE OF RISK FACTORS AND THE DEGREE OF LOSS OF EMPLOYMENT ON THE DEVELOPMENT OF POST-STROKE DEPRESSION AND ANXIETY IN PATIENTS WITH CVA
Bibliographic record
Abstract
Stroke is one of the most common diseases of the nervous system, which is often complicated by the development of anxiety-depressive disorders. The frequency of occurrence and features of the course of depressive disorders in patients with stroke, as well as their relationship with somatic burden, were assessed. A retrospective analysis of 40 case histories of patients with stroke was carried out, a questionnaire survey using questionnaires: Hospital Anxiety and Depression Scale (HADS), Toronto Alexithymic Scale, TAS-26. Depressive disorders of varying severity were detected in more than 70% of the examined patients with stroke. Females are more likely to develop depression and anxiety. Among patients after ischemic stroke and acute myocardial infarction, the proportion of people with moderate and severe depression was higher. It was revealed that the younger the patients who have suffered a stroke, the harder they tolerate the fact of the disease and the more pronounced their depressive state. The severity of functional deficit after stroke directly affects the risk of developing post-stroke anxiety-depressive disorders. Thus, anxiety and depression in patients who are not able to perform work, therefore, released from work duties was manifested by clinically pronounced symptoms in 74.1% of cases, which indicates a significant role of the psychological reaction to the disease.
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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 | 0.000 |
| 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".