MétaCan
Menu
Back to cohort
Record W4200197654 · doi:10.3389/fnagi.2021.757453

Association Between Sleep Disorders and Cognitive Impairment in Middle Age and Older Adult Hemodialysis Patients: A Cross-Sectional Study

2021· article· en· W4200197654 on OpenAlexaboutno aff
Taotao Ru, Yun Bai, Yidan Guo, Pengpeng Ye, Yang Luo

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2021
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
FundersBeijing Municipal Science and Technology Commission
KeywordsPittsburgh Sleep Quality IndexMedicineHemodialysisMontreal Cognitive AssessmentSleep (system call)Internal medicineCross-sectional studyLogistic regressionPolysomnographyProspective cohort studySleep disorderInsomniaCohortPhysical therapyCognitionCognitive impairmentPsychiatrySleep qualityPathology

Abstract

fetched live from OpenAlex

Objective: The aims of the current study were to (1) explore the features of overall poor sleep and specific sleep disorders in Chinese middle age and older adult hemodialysis patients; (2) examine the association between sleep disorders and cognitive impairment (CI) in middle age and older patients undergoing hemodialysis in China. Methods: Data of patients undergoing maintenance hemodialysis were collected from the prospective cohort study of CI in Chinese patients undergoing hemodialysis (Registered in Clinical Trials.gov , ID: NCT03251573). We included 613 patients (mean age = 63.7; SD = 7.8) in this study. We assessed sleep conditions using the Pittsburgh Sleep Quality Index (PSQI) questionnaire and cognitive function by the Chinese Beijing version of the Montreal Cognitive Assessment (MoCA-BJ) scale. Then the association between sleep disorders and CI was evaluated using multivariate logistic regression analysis. Results: The prevalence of sleep disorders in this group of 613 hemodialysis patients was 77.0%. Patients with CI were more inclined to have sleep disorders in specific aspects of sleep latency, habitual sleep efficiency, sleep disturbances, and daytime dysfunction (p < 0.05). In multivariate logistic regression analyses, every 1-point increase in global PSQI score was associated with a 1.2-fold increased risk of CI (adjusted OR = 1.201; 95%CI = 1.123–1.284, p < 0.001). For each specific PSQI, every 1-point increase in sleep disturbances score was associated with a 2.6-fold increased risk of CI (adjusted OR = 2.624; 95%CI = 1.891–3.640, p < 0.001), and every 1-point increase in daytime dysfunction score was associated with a 3.7-fold increased risk of CI (adjusted OR = 3.709; 95%CI = 2.653–5.184, p < 0.001), whereas every 1-point increase in sleep duration score was associated with a decreased risk of CI (adjusted OR = 0.600; 95%CI = 0.434–0.830, p = 0.002). Conclusion: Poor sleep quality especially sleep disturbances, daytime dysfunction, and long sleep duration are associated with CI in middle age and older adult hemodialysis patients. Thus, the early detection of sleep disorders may help identify patients with cognitive impairment among hemodialysis individuals. Clinical Trial Registration: [ Clinical Trials.gov ], identifier [NCT03251573]

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.011
GPT teacher head0.275
Teacher spread0.263 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Aging NeuroscienceSame topicSleep and related disordersFrench-language works237,207