Characteristics of sleep structure in Parkinson's disease patients with hallucinations based on polysomnography
Bibliographic record
Abstract
Hallucination is a common non-motor symptom in patients with Parkinson's disease (PD). Additionally, sleep disorders are associated with an increased risk of hallucinations in PD patients. This study aimed to examine the association between hallucination and objective sleep parameters in PD patients. We retrospectively recruited 278 PD patients who underwent polysomnography and clinical assessments and classified them into non-hallucination and hallucination groups. Hallucinations were observed in 77 older PD patients who had more severe motor symptoms and higher scores on the Non-Motor Symptoms Questionnaire (NMSQ), Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA) but lower scores on the Montreal Cognitive Assessment (MOCA) and PD Sleep Scale (PDSS) than PD patients without hallucinations. Analysis of the polysomnographic variables in patients with hallucinations showed that they exhibited a decrease in total sleep time, sleep efficiency (SE), rapid eye movement (REM) sleep time and slow wave sleep (SWS, N3) time and percentage but a significant increase in wake time after sleep onset (WASO), periodic limb movement index (PLMI) scores, and stage 2 NREM (N2)percentage. Logistic regression analysis revealed that higher NMSQ scores, lower MOCA scores, lower SE, and a lower percentage of N3 sleep were associated with hallucinations in PD patients. Our results suggested that PD patients with hallucinations had worse sleep quality and differences in sleep architecture (measured by polysomnography).
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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 | 0.000 |
| 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".