Rapid Eye Movement (REM) Sleep Behavior Disorder and REM Sleep with Atonia in the Young
Bibliographic record
Abstract
BACKGROUND: Rapid eye movement (REM) sleep behavior disorder (RBD) and REM sleep without atonia (RWA) have assumed much clinical importance with long-term data showing progression into neurodegenerative conditions among older adults. However, much less is known about RBD and RWA in younger populations. This study aims at comparing clinical and polysomnographic (PSG) characteristics of young patients presenting with RBD, young patients with other neurological conditions, and normal age-matched subjects. METHODS: A retrospective chart review was carried out for consecutive young patients (<25 years) presenting with clinical features of RBD; and data were compared to data from patients with epilepsy, attention deficit hyperactivity disorder (ADHD), and autism, as well as normal subjects who underwent PSG during a 2-year-period. RESULTS: Twelve patients fulfilling RBD diagnostic criteria, 22 autism patients, 10 with ADHD, 30 with epilepsy, and 14 normal subjects were included. Eight patients with autism (30%), three with ADHD (30%), one with epilepsy (3.3%), and six patients who had presented with RBD like symptoms (50%) had abnormal movements and behaviors during REM sleep. Excessive transient muscle activity and/or sustained muscle activity during REM epochs was found in all patients who had presented with RBD, in 16/22 (72%) autistic patients, 6/10 (60%) ADHD patients compared to only 6/30 (20%) patients with epilepsy and in none of the normal subjects. CONCLUSION: We observed that a large percentage of young patients with autism and ADHD and some with epilepsy demonstrate loss of REM-associated atonia and some RBD-like behaviors on polysomnography similar to young patients presenting with RBD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".