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Record W4389849377 · doi:10.1016/j.sleep.2023.12.010

Sleep structure assessed by objective measurement in patients with mild cognitive impairment: A meta-analysis

2023· article· en· W4389849377 on OpenAlexaboutno aff
Jianing Wei, Min Wang, Yuanli Guo, Yanjin Liu, Xiaofang Dong

Bibliographic record

VenueSleep Medicine · 2023
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
Fundersnot available
KeywordsSleep (system call)Sleep architectureMeta-analysisMedicineCochrane LibraryDementiaCognitive impairmentCognitionSleep onset latencyAudiologyInternal medicinePolysomnographySleep disorderPsychiatryDisease

Abstract

fetched live from OpenAlex

OBJECTIVES: A meta-analysis was used to explore the characteristic changes in objective sleep structure of patients with mild cognitive impairment (MCI) compared with cognitively healthy older adults. MATERIALS AND METHODS: PubMed, EMBAS, Cochrane Library, Scopus, and Web of Science were searched until November 2023. A literature quality evaluation was performed according to the Newcastle-Ottawa Scale, and a meta-analysis was performed by RevMan 5.3 software. RESULTS: Fifteen studies with 771 participants were finally included. Compared with normal control groups, patients with MCI had a decreased total sleep time by 34.44 min, reduction in sleep efficiency by 7.96 %, increased waking after sleep onset by 19.61 min, and increased sleep latency by 6.97 min. Ten included studies showed that the patients with MCI had increased N1 sleep by 2.72 % and decreased N3 sleep by 0.78 %; however, there was no significant difference between the MCI and control groups in percentage of N2 sleep. Moreover, Twelve included studies reported the MCI groups had shorter REM sleep of 2.69 %. CONCLUSION: Our results provide evidence of abnormal sleep architecture in patients with MCI. As a "plastic state," abnormal sleep architecture may be a promising therapeutic target for slowing cognitive decline and dementia prevention.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.030
GPT teacher head0.294
Teacher spread0.265 · 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 designMeta-analysis
Domainnot available
GenreEmpirical

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

Citations20
Published2023
Admission routes1
Has abstractyes

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