The Effect of Psychological Factors on Cognitive Functions in Stroke Patients With Chronic Fatigue
Bibliographic record
Abstract
Background and Objectives: Cognitive and psychological impairments are among the disabling consequences of chronic stroke. Despite the high prevalence of these impairments in patients with chronic stroke and the significant impact of psychological factors on cognitive factors in other neurological diseases, no study was found to investigate the relationship between psychological factors and cognitive factors in chronic stroke patients with chronic fatigue. Therefore, this study aimed to investigate the relationship between psychological factors and cognitive functions in chronic stroke patients with chronic fatigue Methods: A total of 85 chronic stroke patients with chronic fatigue visited the Rehabilitation Centers of Tehran, Iran, were selected through the simple non-probability sampling method and enrolled in this correlational study. The Fatigue Severity Scale, Beck Depression Inventory, and Beck Anxiety Inventory were used to measure the levels of the fatigue, depression, and anxiety of patients with strokes, respectively. Besides, the cognitive functions of the participants were assessed using the Mini-Mental State Examination, the Montreal Cognitive Assessment, and the Pain Visual Analog Scale. Results: Based on the regression models, the Mini-Mental State Examination and the Montreal Cognitive Assessment explained up to 24.2% and 39.6% of the variance of cognitive functions, respectively. In all step-by-step models, the variables of anxiety, education level, and depression were the strongest predictors of cognitive functions. Conclusion: According to the clinical findings, psychological impairments, such as anxiety can adversely affect cognitive factors in chronic stroke patients with chronic fatigue. Therefore, therapeutic interventions focused on psychological factors may considerably improve the cognitive skills of these patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".