Dreaming and parasomnias - a case with severe parasomnia overlap disorder and its treatment
Bibliographic record
Abstract
Dreams are experiences during sleep that are internally generated by the brain.Dreaming is a common phenomenon during normal sleep and may occur in all sleep stages.The brain is active during sleep, but perceptual connection to the environment is mostly turned off.However, the content of dreams can be affected by both external and internal stimuli.Dream recall is associated with higher frequency brain activity during sleep, typical of REM sleep and arousals.During parasomnias, dream experiences may be disturbed, behaviourally manifested or mixed with waking reality.Parasomnias are sleep disorders characterized by incomplete transitions between sleep and wake.Abnormal motor, sensory or behavioural manifestations of parasomnias occur at sleep onset, within sleep or during arousal from sleep.Various sensory stimuli may be able to disturb sleep causing arousals or partial awakenings typical of parasomnias.Differential diagnosis of parasomnias includes patient report of recalled and enacted dream content, timing and age at onset of parasomnia episodes, witnessed sleep behaviour, and polysomnographic findings about the sleep stage and the mechanism of episodes.In adults, parasomnias may have a significant negative effect on well-being and even violent consequences.A patient case with severe parasomnia overlap disorder and its treatment is presented.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".