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Record W1843415357 · doi:10.1177/070674371005500703

Sleep in Psychiatric Disorders: Where are We Now?

2010· review· en· W1843415357 on OpenAlexaffvenue
Elliott Kyung Lee, Alan B. Douglass

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2010
Typereview
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsRoyal Ottawa Mental Health CentreUniversity of Ottawa
FundersForskningsrådet om Hälsa, Arbetsliv och Välfärd
KeywordsNon-rapid eye movement sleepSleep (system call)PsychologyPsychiatrySleep deprivationAnxietySleep disorderMood disordersSleep debtSlow-wave sleepSchizophrenia (object-oriented programming)CognitionNeuroscienceElectroencephalography

Abstract

fetched live from OpenAlex

Although the precise function of sleep is unknown, decades of research strongly implicate that sleep has a vital role in central nervous system (CNS) restoration, memory consolidation, and affect regulation. Slow-wave sleep (SWS) and rapid eye movement (REM) sleep have been of significant interest to psychiatrists; SWS because of its putative role in CNS energy recuperation and cognitive function, and REM sleep because of its suggested involvement in memory, mood regulation, and possible emotional adaptation. With the advent of the polysomnogram, researchers are now beginning to understand some of the consequences of disrupted sleep and sleep deprivation in psychiatric disorders. The same neurochemistry that controls the sleep-wake cycle has also been implicated in the pathophysiology of numerous psychiatric disorders. Thus it is no surprise that several psychiatric disorders have prominent sleep symptoms. This review will summarize normal sleep architecture, and then examine sleep abnormalities and comorbid sleep disorders seen in schizophrenia, as well as anxiety, cognitive, and substance abuse disorders.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.052
GPT teacher head0.331
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations67
Published2010
Admission routes2
Has abstractyes

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