Categorising Visual Hallucinations in Early Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: Visual hallucinations (VHs) are common in Parkinson's disease (PD), with prevalence ranging from 27-50% in cross-sectional cohorts of patients with well-established disease. However, minor hallucinations may occur earlier in the disease process than has been previously reported. OBJECTIVE: We sought to categorise VHs in a cohort of newly diagnosed PD patients and establish their relationship to other clinical features. METHODS: Newly diagnosed PD participants (n = 154) were recruited as part of the Incidence of Cognitive Impairment in Cohorts with Longitudinal Evaluation in PD (ICICLE-PD) study. Participants completed the Movement Disorders Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS III), Montreal Cognitive Assessment (MoCA) and Parkinson's Disease Questionnaire (PDQ-39) to assess motor severity, cognition and quality of life (QoL), respectively. VHs were classified using the North East Visual Hallucinations Inventory. Hierarchical regression was used to build predictive models of motor severity, QoL and cognition. RESULTS: 22% (n = 34) of participants experienced recurrent VHs with minor VHs being most frequently reported (64.7% of hallucinators). Complex VHs were present in 32.4% of hallucinating participants. Linear regression showed VHs predicted poorer PDQ-39 and MoCA scores (β= 0.201, p = 0.006 and β= - 0.167, p = 0.01, respectively) but not motor severity (p > 0.05). CONCLUSIONS: Over a fifth of people with newly diagnosed PD reported recurrent VHs; minor hallucinations were the most common, although a small proportion reported complex VHs. Recurrent VHs were found to be a significant independent predictor of cognitive function and QoL but not motor severity. Our findings highlight the importance of screening for VHs at diagnosis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".