Psychopathology and NREM sleep parasomnias: A systematic review
Bibliographic record
Abstract
Non-rapid eye movement (NREM) sleep parasomnias are abnormal motor and/or emotional behaviors originating from "deep" slow-wave sleep and with a multifactorial origin. The relationship between NREM parasomnias and psychopathology has been a topic of ongoing debate, but a comprehensive and systematic perspective has been lacking. This systematic review, conducted according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA-P) guidelines, aims to fill this gap in the literature. Databases including PubMed, Scopus, Embase, and Web of Science were searched from their inception until March 2024. Only studies written in English were included. We selected case-control studies that reported either psychopathological or neurodevelopmental data in NREM sleep parasomnias, or NREM sleep parasomnia data across different mental disorders, across children and adults. Our review found that psychopathological and neurodevelopmental issues are common in NREM parasomnias, with a higher prevalence in affected patients compared to non-affected individuals. Additionally, NREM parasomnias are more common among patients with various psychopathological conditions than in the general population. Medications did not significantly bias these results. These findings suggest that psychopathological aspects should become a core focus of research and treatment strategies for NREM parasomnias.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".