Differential effects of sleep deprivation on sleepwalking: Role of demographic and clinical profiles
Bibliographic record
Abstract
BACKGROUND: Although sleepwalking is one of the most prevalent and potentially injurious of the NREM parasomnias, it is still diagnosed primarily based on the patient's clinical history. Early pilot work suggested that sleep deprivation protocols could help obtain a polysomnographically-based (PSG) diagnosis of sleepwalking, but larger studies remain lacking. METHODS: We compared baseline PSG recordings with those obtained after 25hrs of sleep deprivation in a cohort of 124 consecutively assessed adult sleepwalkers. RESULTS: When compared to baseline recordings, post-sleep deprivation PSG assessments resulted in nearly twice as many somnambulistic episodes being recorded in the laboratory and significantly increased the proportion of patients (from 48 % to 63 %) experiencing at least one lab-based episode. Moreover, while 17 % of patients experienced a sleepwalking event exclusively during recovery sleep, only 2 % of patients did so solely at baseline. Sleep deprivation had similar facilitating effects on patents' somnambulistic events regardless of age of onset and positive versus negative family history for sleepwalking. Younger age and higher home episode frequency both predicted a positive response to sleep deprivation. A separate group of 17 patients with comorbid sleep disorders showed a similar increase in their proportion experiencing at least one episode during recovery sleep. CONCLUSION: The results from this large series of sleepwalkers provide strong support for the use of sleep deprivation in facilitating the occurrence of somnambulistic events in the sleep laboratory.
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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 | 0.003 |
| 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) | 0.002 | 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".