Do dreams tell the future? Dream content as a predictor of cognitive deterioration in Parkinson’s disease
Bibliographic record
Abstract
Cross-sectional studies suggest a correlation between alterations in dream content reports and executive dysfunction tests in Parkinson's disease (PD), but this has not been assessed in longitudinal studies. Our objective was to assess the predictive value of dream content for progression of cognitive dysfunction in PD. We prospectively addressed all consecutive, non-demented patients with PD attending an outpatient clinic during a 1-year period. Dream reports were collected at baseline by means of a dream diary and analysed according to the Hall and Van de Castle system. Patients were assessed at baseline for rapid eye movement sleep behaviour disorder, motor stage, mood disorder and psychosis. The Montreal Cognitive Assessment (MoCA) was applied at baseline and 4 years later. Linear regression analysis was used to the test the relation between each dream index (predictors), demographic and other motor and non-motor variables (covariates), and change in MoCA scores (dependent variable). In all, 58 patients were assessed at both time points and 23 reported at least one dream (range 1-27, total 148). Aggression, physical activities, and negatively toned content predominated in dream reports. The MoCA scores decreased significantly from baseline to follow-up. In the multivariate model, negative emotion index was the strongest predictor of cognitive decline. We found a significant positive association between negative emotions in dreams at baseline and subsequent reduction in MoCA scores. These findings suggest that some dream content in patients with PD could be considered a predictor of cognitive decline, independent of other factors known to influence either dream content or cognitive deterioration.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".