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Record W2150453361 · doi:10.1503/cmaj.060792

Sleep and aging: 1. Sleep disorders commonly found in older people

2007· review· en· W2150453361 on OpenAlexvenueno aff
Norman Wolkove, O. Elkholy, Marc Baltzan, M J Palayew

Bibliographic record

VenueCanadian Medical Association Journal · 2007
Typereview
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsnot available
Fundersnot available
KeywordsPolysomnographyMedicineInsomniaSleep disorderSleep (system call)NarcolepsyObstructive sleep apneaEveningSleep apneaRestless legs syndromeNon-rapid eye movement sleepPopulationPsychiatryPediatricsAudiologyPhysical medicine and rehabilitationPhysical therapyApneaNeurologyInternal medicineElectroencephalography

Abstract

fetched live from OpenAlex

Aging is associated with several well-described changes in patterns of sleep. Typically, there is a phase advance in the normal circadian sleep cycle: older people tend to go to sleep earlier in the evening but also to wake earlier. They may also wake more frequently during the night and experience fragmented sleep. The prevalence of many sleep disorders increases with age. Insomnia, whether primary or secondary to coexistant illness or medication use, is very common among elderly people. Rapid eye movement (REM) sleep behaviour disorder and narcolepsy, although less common, are frequently not considered for this population. Periodic leg-movement disorder, a frequent cause of interrupted sleep, can be easily diagnosed with electromyography during nocturnal polysomnography. Restless legs syndrome, however, is diagnosed clinically. Snoring is a common sleep-related respiratory disorder; so is obstructive sleep apnea, which is increasingly seen among older people and is significantly associated with cardio-and cerebrovascular disease as well as cognitive impairment.

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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.005

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 head0.037
GPT teacher head0.339
Teacher spread0.302 · 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

Citations386
Published2007
Admission routes1
Has abstractyes

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