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Record W2047806461 · doi:10.1097/mcp.0000000000000103

Mechanisms of REM sleep in health and disease

2014· review· en· W2047806461 on OpenAlexaff
Jimmy J. Fraigne, Kevin P. Grace, Richard L. Horner, John Peever

Bibliographic record

VenueCurrent Opinion in Pulmonary Medicine · 2014
Typereview
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNon-rapid eye movement sleepREM sleep behavior disorderSleep (system call)MedicineNarcolepsySleep paralysisNeuroscienceNeuroscience of sleepSleep spindleCataplexyRapid eye movement sleepNeurologyPolysomnographyPsychologyEye movementApneaPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Our understanding of rapid eye movement (REM) sleep and how it is generated remains a topic of debate. Understanding REM sleep mechanisms is important because several sleep disorders result from disturbances in the neural circuits that control REM sleep and its characteristics. This review highlights recent work concerning how the central nervous system regulates REM sleep, and how the make up and breakdown of these REM sleep-generating circuits contribute to narcolepsy, REM sleep behaviour disorder and sleep apnea. RECENT FINDINGS: A complex interaction between brainstem REM sleep core circuits and forebrain and hypothalamic structures is necessary to generate REM sleep. Cholinergic activation and GABAergic inhibition trigger the activation of subcoeruleus neurons, which form the core of the REM sleep circuit. SUMMARY: Untimely activation of REM sleep circuits leads to cataplexy - involuntary muscle weakness or paralysis - a major symptom of narcolepsy. Degeneration of the REM circuit is associated with excessive muscle activation in REM sleep behaviour disorder. Inappropriate arousal from sleep during obstructive sleep apnea repeatedly disturbs the activity of sleep circuits, particularly the REM sleep circuit.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.199
GPT teacher head0.452
Teacher spread0.254 · 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 teacher head, not a consensus.

Study designOther design
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

Citations21
Published2014
Admission routes1
Has abstractyes

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