Polysomnographic diagnosis of sleepwalking: Effects of sleep deprivation
Bibliographic record
Abstract
OBJECTIVE: Somnambulism affects up to 4% of adults and constitutes one of the leading causes of sleep-related violence and self-injury. Diagnosing somnambulism with objective instruments is often difficult because episodes rarely occur in the laboratory. Because sleep deprivation can precipitate sleepwalking, we aimed to determine the effects of 25 hours of sleep deprivation on the frequency and complexity of somnambulistic episodes recorded in the laboratory. METHODS: Thirty consecutive sleepwalkers were evaluated prospectively by video-polysomnography for one baseline night and during recovery sleep after 25 hours of sleep deprivation. Ten sleepwalkers with a concomitant sleep disturbance were investigated with the same protocol. RESULTS: Sleepwalkers experienced a significant increase in the mean frequency of somnambulistic episodes during postdeprivation recovery sleep. Postsleep deprivation also resulted in a significantly greater proportion of patients experiencing more complex forms of somnambulism. Sleep deprivation was similarly effective in 9 of the 10 patients presenting with a comorbid sleep disturbance. Combining data from all 40 patients shows that whereas 32 episodes were recorded from 20 sleepwalkers (50%) at baseline, recovery sleep resulted in 92 episodes being recorded from 36 patients (90%). INTERPRETATION: The findings support the view that sleepwalkers suffer from a dysfunction of the mechanisms responsible for sustaining stable slow-wave sleep and suggest that these patients are particularly vulnerable to increased homeostatic sleep pressure. Strong evidence is provided that 25 hours of sleep deprivation can be a valuable tool that facilitates the polysomnographically based diagnosis of somnambulism in predisposed patients.
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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 | 0.000 | 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".