The Relationship Between Psychological Factors and Cognitive Function in Patients With Parkinson’s Disease Who Have Chronic Fatigue
Bibliographic record
Abstract
Background and Objectives: Cognitive and psychological disorders are among the most debilitating complications of Parkinson’s Disease (PD). Despite the high prevalence of these disorders in patients with PD and the important effect of psychological factors on cognitive factors in other neurological diseases, no study was found on the relationship between psychological factors and cognitive function in patients with PD who have chronic fatigue. The present study was conducted to investigate the relationship between psychological factors and cognitive function in patients with PD and chronic fatigue. Methods: In the present cross-sectional study, a total of 73 patients with PD who had chronic fatigue were selected by non-random convenience sampling method from those visiting Tehran’s rehabilitation centers in 2019. The following questionnaires were used: Fatigue Severity Scale (FSS) for fatigue, Beck’s Depression Inventory for depression, Beck’s Anxiety Inventory for anxiety, and Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) for cognitive function, as well as Unified Parkinson’s Disease Rating Scale (UPDRS-III) and Pain Visual Analog Scale (Pain VAS). Results: The regression models explained the cognitive function variance by a maximum of 1.43% in MMSE and 8.79% in MoCA. In all stepwise models of cognitive function, anxiety was the strongest predictor of cognitive function followed by age and UPDRS-III score. Conclusion: The results of this study indicated that anxiety as the strongest predictor can affect cognitive function in patients with PD who have chronic fatigue. Hence, therapeutic interventions focusing on psychological factors may be particularly important for improving cognitive function in 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".