Dream Content Predicts Motor and Cognitive Decline in Parkinson's Disease
Bibliographic record
Abstract
ABSTRACT Background Dream content alterations in Parkinson's disease (PD) are associated with motor and cognitive dysfunction cross‐sectionally. Although recent studies suggest abnormal dream content in PD might also predict cognitive decline, the relationship between dream content and motor decline in PD remains unknown. Objective To investigate whether abnormal dream content in PD predicts both motor and cognitive decline. Methods Data were obtained from the Parkinson's Progression Markers Initiative cohort study. Patients were evaluated at baseline and at the 60‐month follow‐up, with validated clinical scales, including the REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ), Montreal Cognitive Assessment (MoCA), and the Movement Disorder Society–Unified Parkinson's Disease Rating Scale Part III (MDS‐UPDRS III). Patients were dichotomized using RBDSQ item 2, which inquires whether they frequently experience aggression in their dreams. Regression analyses were used to assess whether frequent aggressive dreams at baseline predicted longitudinal changes in MDS‐UPDRS III and MoCA scores as well as progression to Hoehn and Yahr stage 3 (H&Y ≥ 3) and cognitive impairment. Results Of the patients, 58/224 (25.9%) reported frequent aggressive dreams at baseline. Aggressive dreams predicted a faster increase in MDS‐UPDRS III scores (β = 4.64; P = 0.007) and a faster decrease in MoCA scores (β = −1.49; P = 0.001). Furthermore, they conferred a 6‐fold and 2‐fold risk for progressing to H&Y ≥ 3 (odds ratio [OR] = 5.82; P = 0.005) and cognitive impairment (OR, 2.35; P = 0.023) within 60 months. These associations remained robust when adjusting for potential confounders. Conclusions This study demonstrates for the first time that frequent aggressive dreams in newly diagnosed PD may independently predict early motor and cognitive decline.
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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 | 0.003 |
| 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 | 0.001 | 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".