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Record W2372121956

Relationship between sleep quality and cognitive impairment in elderly veterans

2013· article· en· W2372121956 on OpenAlexaboutno aff
Luning Wang

Bibliographic record

VenueTranslational Medicine Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Risk Factors
Canadian institutionsnot available
Fundersnot available
KeywordsPittsburgh Sleep Quality IndexMontreal Cognitive AssessmentCognitive impairmentMedicineSleep qualitySleep (system call)CognitionGerontologySleep disorderPhysical therapyIncidence (geometry)Psychiatry
DOInot available

Abstract

fetched live from OpenAlex

Objective To explore the relationship between sleep quality and cognitive impairment in elderly veterans.Methods Three hundred twenty five veterans aged over 60 years from 6 military sanatorium in Beijing city were evaluated with Pittsburgh sleep quality index(PSQI),Mini-mental state examination(MMSE) and Montreal cognitive assessment(MoCA)from January 2010 to December 2011.Results The mean score of PSQI was(5.66±4.27),and the amount of sleep disorders was 102,the prevalence was 31.38%.The amount of veterans with cognitive impairment was 95,the prevalence was 29.23%,the amount of veterans with Mild Cognitive Impairment and Alzheimer′s disease were 82 and 13 respectively.The occurrence of cognitive impairment was higher in sleep disorders patients than that in control.The correlation analysis was negative between some items of PSQI and MoCA.Conclusion The incidence of sleep disorder in elderly veterans was high,and there was correlation between sleep quality and 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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.079
GPT teacher head0.375
Teacher spread0.296 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

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