Prevalence and risk factors of hallucination in patients with Parkinson disease
Bibliographic record
Abstract
Objective To investigate the prevalence and risk factors of hallucination in patients with Parkinson disease(PD),and to analysis the relationship between hallucination and different types of PD. Methods We recruited 163 patients with PD from outpatient department in Beijing Tiantan Hospital. The subjects were divided into two groups, the hallucination group and the non-hallucination group. We assessed both the two groups respectively with UPDRS, Hohen-Yahr rank, Hamilton Depression Scale(HAMD), Pittsburgh Sleep Quality Index(PSQI), Fagitue Severity Scale(FSS), Parkinson fatigue scale(PFS),Mini-Mental State Examination(MMSE) and Montreal Cognitive Assessment (MoCA). Then we compared the difference between the two groups, statistical cluster analysis was used to study the relationship between heterogeneity and hallucination. Results In our 163 patients with PD, 20 of them (12.27%) had hallucination. The hallucination and non-hallucination groups had no difference in sex, age, age of onset, duration of illness, long-term complications and the scores of UPDRS, PSQI, FSS, PFS, MMSE and MoCA (P0.05). The proportion of dyskinesia (35.0%) in hallucination group was significantly higher than that (9.1%) in the non-hallucination group. There were also significant differences in scores of postural stability, H-Y, UPDRS I, UPDRS II, HAMD and Levodopa Equivalents between the two groups. Logistic regression revealed that depression was the independent risk factor for hallucination in PD patients. Conclusions Hallucination is one of the common non-motor symptom in all types of PD. Postural stability, the severity of disease, Levodopa Equivalents and depression are all important risk factors in PD hallucination, and depression is the only independent risk factor.
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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".