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Record W2054866242 · doi:10.1177/1550059413496778

Grand Total EEG as a Predictive Biomarker for Cognitive Impairment Severity in Cerebral Infarcts of Chinese

2013· article· en· W2054866242 on OpenAlexaboutno aff
Xiaohong Wang, Yongxin Sun, Jiuhan Zhao, Aihua Xu

Bibliographic record

VenueClinical EEG and Neuroscience · 2013
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsnot available
Fundersnot available
KeywordsElectroencephalographyMontreal Cognitive AssessmentCognitionDementiaCognitive impairmentNeuropsychologyAudiologySpearman's rank correlation coefficientRank correlationCorrelationInternal medicineBiomarkerClinical Dementia RatingMedicinePsychologyDiseasePsychiatry

Abstract

fetched live from OpenAlex

Cerebral infarct (CI) is a common disease of older adults, which increases the risk for cognitive impairment or dementia. CI-associated mild cognitive impairment is a potential prodromal stage of serious cognitive impairment. The grand total EEG (GTE) score is a rating scale for clinical electroencephalography (EEG) analyses, which is useful in the evaluation of different types of cognitive impairment. Sixty-five patients with CI underwent neuropsychological testing and resting state EEG. Spearman rank correlation analysis was used to investigate the relationship between a short version of the GTE score and severity of cognitive impairment in CI. Significant correlations with deteriorating cognition (combined Montreal Cognitive Assessment/clock drawing test) were found for the overall short GTE score (Spearman rank correlation, p = -0.61, r = -0.88491, P = 0.009) and for the subscore "Frequency of Rhythmic Background Activity" (p = -0.63, r = -0.92559, P = 0.007). In conclusion, the GTE short score and Frequency of Rhythmic Background Activity were increased with the deteriorating cognitive impairment in patients with CI.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.472
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.042
GPT teacher head0.356
Teacher spread0.313 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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