Cognitive Correlates of Visual and Minor Hallucinations in Parkinson’s Disease
Bibliographic record
Abstract
INTRODUCTION: Psychosis is one of the incapacitating nonmotor symptoms of Parkinson's disease (PD). Although several risk factors that include older age, rapid eye movement sleep behavior disorder, depression, and cognitive dysfunction have been identified, the exact neural correlates remain elusive. As cognitive impairment has a close association with psychosis in PD, it is useful to know the spectrum of cognitive impairment in PD patients with psychosis (PD-P). METHODS: This cross-sectional study compared various cognitive parameters of PD-P (visual/minor hallucinations) and PD patients with no psychosis (PD-NP). A neuropsychological battery encapsulating several cognitive domains (executive, visuospatial, learning, and memory) was used for the cognitive assessment of 37 PD-P and 51 PD-NP patients who were matched for age, gender, education, and disease duration. RESULTS: The two groups were comparable in terms of disease severity and stage. Although the groups had a comparable mean score on Montreal cognitive assessment, the PD-P group performed poorly in tests focused on executive function (color trail test, forward digit span), verbal learning and memory (Rey auditory and verbal learning test), and visuospatial functions (complex figure test, corsi block tapping test). Those with complex visual hallucinations performed poorly in the color trial test (part A) compared to those with minor hallucinations. CONCLUSION: Psychosis is associated with a multidomain cognitive dysfunction in PD. All PD patients should undergo detailed cognitive assessment as cognitive dysfunction may be a marker of psychosis in the future. Additional longitudinal studies are warranted to obtain detailed insights into this issue.
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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 | 0.001 |
| 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".