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Record W2473910478 · doi:10.5539/gjhs.v9n2p119

Evaluation of the Effect of Time Change in Cognitive Function in Volunteers in Tehran

2016· article· en· W2473910478 on OpenAlexvenueno aff
Marjan Erfani, Hedayat Sahraei, Zahra Bahari, Gholam Hossein Meftahi, Boshra Hatef, Alireza Mohammadi, Seyed Hossein Hosseini

Bibliographic record

VenueGlobal Journal of Health Science · 2016
Typearticle
Languageen
FieldPsychology
TopicPsychological and Temporal Perspectives Research
Canadian institutionsnot available
FundersBaqiyatallah University of Medical Sciences
KeywordsCognitionMedicineEffects of sleep deprivation on cognitive performancePsychologyInternal medicineEndocrinologyPsychiatry

Abstract

fetched live from OpenAlex

<strong></strong><p><strong>BACKGROUND:</strong> Time change (which can lead to sleep duration decrements) can lead to brain dysfunction if repeated. In the present study, cognitive functions of the volunteers were evaluated before and after the time changes in Tehran.</p><p><strong>METHODS:</strong> Eleven, voluntary healthy persons (21±2 year old) were evaluated for their cognitive functions including sustain attention, reaction time, and mental fatigue twenty-one days before the time changes and thirty-eight days after time change using PASAT software. In addition, plasma cortisol level was measured before and after the time changes.</p><p><strong>RESULTS:</strong> After the time changes salivary cortisol concentration increase, but general mental health was decreased. Sustain attention was shortened after time change which was significantly different compared with before the time changes. Reaction time was increased after the time changes in comparison with the before the time changes, but was not statistically significant. In addition, mental fatigue was increased after the time changes.</p><p><strong>CONCLUSION:</strong> It seems that time change may reduce brain cognitive functions which are manifested by general mental health, sustain attention reduction, reaction time as well as mental fatigue.</p>

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)0.013
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 score0.163
Threshold uncertainty score0.445

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.121
GPT teacher head0.490
Teacher spread0.369 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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