[Neuropsychiatric problems in patients with Parkinson's disease].
Bibliographic record
Abstract
OBJECTIVE: To survey the prevalence and distribution of neuropsychiatric problems in patients with Parkinson's disease (PD), and to investigate their effects on life quality and the interactions among different neuropsychiatric problems. METHODS: Unified Parkinson's disease rating scale (UPDRS) part III, dyskinesia and motor fluctuation subscale of UPDRS part IV, mini-mental state examination (MMSE) ,Montreal Cognitive Assessment (MoCA), Hamilton rate scale of depression (HRSD), Hamilton anxiety scale (HAMA), digit span (DS), and 39 item Parkinson's disease questionnaire (PDQ-39) were used to assess the motor symptoms and neuropsychiatric problems in 116 PD patients, 66 males and 50 females, aged (67 +/- 9) (50-90), with the course of disease of 5 +/- 4 years (0.5--18 years). Spearman rank order correlation and hierarchical regressions of the major statistical procedures were employed. RESULTS: Various neuropsychiatric problems were found in the PD patients. The neuropsychiatric problems, such as depression, anxiety, apathy, attention deficit disorder, and cognitive deficits, were correlated with the UPDRS III score and Hoehn-Yahr stage, but not correlated with the course of disease ( all P > 0.05). Hallucination was not correlated with any factors (all P > 0.05). There were some correlations among different neuropsychiatric problems. Hierarchical regression revealed that different neuropsychiatric problems showed significant effects on the quality of life after controlling the motor symptoms. Depression (deltaR2 = 19.1%, P < 0.01) and apathy (deltaR2 = 17.0%, P < 0.01) exerted the most powerful influence in causing poor quality of life. CONCLUSION: Neuropsychiatric problems are common a in PD patients Their effects on the poor quality of life are no less than that of motor symptoms and should be recognized and treated well.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.001 |
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".