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Record W3013449452 · doi:10.1097/md.0000000000019667

Both short and long sleep durations are associated with cognitive impairment among community-dwelling Chinese older adults

2020· article· en· W3013449452 on OpenAlexaboutno aff
Gongwu Ding, Jinlei Li, Zhiwei Lian

Bibliographic record

VenueMedicine · 2020
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
FundersFundamental Research Funds for the Central Universities
KeywordsMedicineConfoundingCognitionOdds ratioLogistic regressionSleep (system call)Cross-sectional studyGerontologyObservational studyCognitive impairmentDemographyAssociation (psychology)Montreal Cognitive AssessmentPsychiatryPsychologyInternal medicine

Abstract

fetched live from OpenAlex

This study aims to examine the association between sleep duration and cognitive impairment in community-dwelling Chinese older adults.The associations between sleep duration and cognitive function have been widely studied across various age ranges but are of particular importance among older adults. However, there are inconsistent findings regarding the relationship between sleep duration and cognitive function in the literature.This study is an observational cross-sectional study. We analyzed data from 1115 Chinese individuals aged 60 and older from 3 Chinese communities (Beijing, Hefei, and Lanzhou). Cognitive impairment was defined as a Mini-Mental State Examination total score less than 24 points. Odds ratios (ORs) of associations were calculated and adjusted for potential confounders in logistic regression models.The prevalence of cognitive impairment was 25.7% (n = 287). Controlling for all demographic, lifestyle factors, and coexisting conditions, the adjusted OR for cognitive impairment was 2.54 (95% CI = 1.70-3.80) with <6 hours sleep and 2.39 (95% CI = 1.41-4.06) with >8 hours sleep.Both short and long sleep durations were related to worse cognitive function among community-dwelling Chinese elderly adults.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.275
Teacher spread0.261 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations30
Published2020
Admission routes1
Has abstractyes

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