Analysis of Clinical Features and Influencing Factors of Depression and Cognitive Dysfunction in Parkinson’s Patients
Bibliographic record
Abstract
Objective: To explore the clinical characteristics and analyze the influencing factors of depression and cognitiveimpairment in Parkinson’s patients. Methods: 127 patients with Parkinson’s disease who were treated in two hospitals from March 2021 to March 2023 were selected for this study. The assessment of depression was conducted using the Geriatric Depression Self-Rating Scale (GDS). In addition, cognitive function was evaluated through the Montreal Cognitive Assessment (MOCA), and the clinical attributes of patients with cognitive impairment were examined. The study also aimed to analyze the factors that influence cognitive impairment in this patient population. Results: The progression of the disease, presence of cognitive impairment, and the Hoehn-Yahr stage were identified as significant influencing factors for depression among the 127 patients with Parkinson’s disease (P < 0.05). The factors that influence cognitive impairment included the level of education, Hoehn-Yahr stage, and depression (P < 0.05). Conclusion: Depression and cognitive dysfunction is relatively common in patients with Parkinson’s disease. The occurrence of depression and cognitive dysfunction varies significantly based on factors such as the duration of the disease, varying drug dosages, and the severity of the condition. Therefore, it is necessary to treat patients with Parkinson’s 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 | 0.001 |
| 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".