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Record W3212749365 · doi:10.1017/s1041610221000466

Late-life sleep duration associated with amnestic mild cognitive impairment

2021· article· en· W3212749365 on OpenAlexaboutno aff
Mengya Yuan, Bo Hong, Wei Zhang, An Liu, Jinghua Wang, Yuanyuan Liu, Yan Feng, Shifu Xiao, Hua Xu, Tao Wang

Bibliographic record

VenueInternational Psychogeriatrics · 2021
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsSleep (system call)Duration (music)Odds ratioAssociation (psychology)MedicineConfidence intervalAudiologyLogistic regressionPsychologyInternal medicine

Abstract

fetched live from OpenAlex

Objective:To examine the association between sleep duration in different stages of life and amnestic mild cognitive impairment (aMCI).Design, setting, and participants:A total of 2472 healthy elderly and 505 patients with aMCI in China were included in this study. The study analyzed the association between aMCI and sleep duration in different stages of life.Measurements:We compared sleep duration in different stages of life and analyzed the association between Montreal Cognitive Assessment scores and sleep duration by curve estimation. Logistic regression was used to evaluate the association between aMCI and sleep duration.Results:In the analysis, there were no results proving that sleep duration in youth (P = 0.719, sleep duration < 10 hours; P = 0.999, sleep duration ≥ 10 hours) or midlife (P = 0.898, sleep duration < 9 hours; P = 0.504, sleep duration ≥ 9 hours) had a significant association with aMCI. In the group sleeping less than 7 hours in late life, each hour more of sleep duration was associated with approximately 0.80 of the original risk of aMCI (P = 0.011, odds ratio = 0.80, 95% confidence interval = 0.68–0.95).Conclusions:Among the elderly sleeping less than 7 hours, there is a decreased risk of aMCI for every additional hour of sleep.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.296
Teacher spread0.282 · 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 designObservational
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

Citations12
Published2021
Admission routes1
Has abstractyes

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