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Record W2992846871 · doi:10.17816/pmj30614-19

QUALITY OF SLEEP AND COGNITIVE STATUS IN ACUTE PERIOD OF INSULT AMONG PATIENTS WITH MINIMUM MOTOR DEFICIENCY

2013· article· en· W2992846871 on OpenAlexaboutno aff
А. А. Кулеш, Кулеш Алексей Александрович, T V Lapaeva, Лапаева Татьяна Викторовна, В. В. Шестаков, Шестаков Владимир Васильевич

Bibliographic record

VenuePerm Medical Journal · 2013
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInsultCognitionMontreal Cognitive AssessmentVerbal fluency testSleep (system call)NeuropsychologyAudiologyCognitive impairmentPsychiatry

Abstract

fetched live from OpenAlex

Aim. To assess the correlation between the subjective quality of sleep and the cognitive status in the acute period of insult among patients with minimum motor deficiency. Materials and methods. 67 patients (42 men and 25 women, aged 33–75 years) were examined in the acute period of insult. Neuropsychological study included the following tests: Mini-mental State Examination (MMSE), Frontal Assessment Battery (FAB), Montreal Cognitive Assessment (MoCA), Watch Drawing Test (WDT), Words Test (5) (WT), Schulte Table (ST) and categorical Verbal Fluency Test (VF); the quality of sleep was assessed using Pittsburg Sleep Quality Index (PSQI) determination. Results. Neurodynamic, dysmnestic and mixed types of disorders were singled out on the basis of neuropsychical study. Conclusion. The cognitive status and subjective quality of sleep were assessed in 67 patients in the acute period of insult. After insult, patients had a reduced quality of sleep regarding the time of falling asleep, duration, sleep disturbance and day dysfunction. Low quality of sleep correlated with lower indices of verbal and regulatory processes. The most marked sleep disturbances were detected in patients with mixed and dysmnestic variants of cognitive deficiency.

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 score0.058
Threshold uncertainty score1.000

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 integrity0.0000.000
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 head0.010
GPT teacher head0.279
Teacher spread0.270 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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