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Study on the relationship between cognitive function and sleep structure in patients with idiopathic rapid eye movement sleep behavior disorder

2023· article· en· W6966418977 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldMedicine
TopicRestless Legs Syndrome Research
Canadian institutionsnot available
Fundersnot available
KeywordsPolysomnographySleep (system call)CognitionEye movementStroop effectNeuropsychologyRapid eye movement sleepVerbal fluency testNon-rapid eye movement sleep

Abstract

fetched live from OpenAlex

Objective To summarize the characteristics of cognitive function and sleep structure in patients with idiopathic rapid eye movement sleep behavior disorder (iRBD) and to explore the correlation between them. Methods Total 73 patients with iRBD who visited Fujian Medical University Union Hospital from August 2018 to August 2021 were included, all of whom underwent neuropsychological tests and polysomnography monitoring. Pearson and partial correlation analyses were used to explore correlation between cognitive function and sleep structure parameters. Results 1) Neuropsychological tests: iRBD patients had lower Mini ⁃ Mental State Examination (MMSE; t = ⁃ 3.703, P = 0.000), Montreal Cognitive Assessment (MoCA; t = ⁃ 4.811, P = 0.000), Verbal Fluency Test (VFT)⁃number of correct words (t = ⁃ 2.171, P = 0.032) than control group. Trail Making Test (TMT)⁃A (t = 2.500, P = 0.014), TMT⁃B (t = 2.430, P = 0.016) and Stroop Color Word Test (SCWT)⁃A (t = 2.507, P = 0.013), SCWT⁃B (t = 15.042, P = 0.000), SCWT⁃ C (t = 27.228, P = 0.000) completion times were longer than control group. 2) Polysomnography monitoring: total sleep time (t = 2.699, P = 0.008), sleep efficiency (Z = ⁃ 2.103, P = 0.035), number of sleep stage transitions (t = 3.965, P = 0.000), proportion of non⁃rapid eye movement sleep stage 1 (N1 stage) to total sleep time (t = 2.887, P = 0.005), periodic limb movement of sleep index (PLMSI; t = ⁃ 2.917, P = 0.004), periodic limb movement⁃related arousal index (Z = ⁃ 2.291, P = 0.022), and total arousal index (Z = ⁃ 2.609, P = 0.009) were higher than those in control group, and the time of wakefulness after sleep was less than those in control group (t = ⁃ 2.230, P = 0.027). 3) Correlation between cognitive function and sleep structure parameters: MoCA score was negatively correlated with the percentage of N1 stage (r = ⁃ 0.184, P = 0.035), and positively correlated with the percentage of N2 stage (r = 0.173, P = 0.049); SCWT⁃B completion time was negatively correlated with the number of awakenings (r = ⁃ 0.186, P = 0.033); SCWT⁃C completion time was positively correlated with the number of sleep phase transitions (r = 0.212, P = 0.015) and the percentage of N1 stage (r = 0.181, P = 0.039); VFT⁃number of correct words was negatively correlated with the periodic limb movement of wake index (PLMWI; r = ⁃ 0.216, P = 0.018); the number of correct China⁃Rey Auditory Verbal Learning Test (C⁃RAVLT) immediate recall was positively correlated with the rapid eye movement (REM) sleep latency (r = 0.183, P = 0.045); the number of correct C⁃RAVLT delay recall was negatively correlated with the PLMWI (r = ⁃ 0.196, P = 0.032) and PLMSI (r = ⁃ 0.180, P = 0.049); the Clock Drawing Test was positively correlated with sleep latency (r = 0.192, P = 0.035) and REM sleep latency (r = 0.199, P = 0.029), and negatively correlated with the percentage of REM sleep (r = ⁃ 0.189, P = 0.038). The Beck Depression Inventory (BDI) score was positively correlated with total sleep time (r = 0.347, P = 0.000), sleep efficiency (r = 0.319, P = 0.000) and the percentage of REM sleep (r = 0.204, P = 0.026), and negatively correlated with the time of wakefulness after sleep (r = ⁃ 0.280, P = 0.002) and the percentage of N1 stage (r = ⁃ 0.299, P = 0.001). Conclusions Patients with iRBD have cognitive dysfunction mainly manifested by decreased attention, executive function and language function, and sleep structural disorders may affect the cognitive function of such patients.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.010
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.262
GPT teacher head0.530
Teacher spread0.267 · 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".

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Citations0
Published2023
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