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Record W2350967190

The value of auditory event-related potentials in the assessment of cognitive function of patients with subcortical ischemic vascular disease

2011· article· en· W2350967190 on OpenAlexaboutno aff
Ran Zhang

Bibliographic record

VenueZhongguo kangfu yixue zazhi · 2011
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsnot available
Fundersnot available
KeywordsAudiologyCognitionMontreal Cognitive AssessmentNeuropsychologyEvent-related potentialPsychologyLatency (audio)AbnormalityMedicineCognitive impairmentPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

Objective:To observe the abnormality of the long latency components and the subcomponents of N2 complex of auditory event-related potentials(ERPs) in the aged patients with subcortical ischemic vascular disease (SIVD),and evaluate their values in assessing the cognitive function of SIVD. Method:The SIVD group included 8 patients clinically diagnosed with SIVD,and the control group included 8 healthy subjects whose sex and age matched with those in SIVD group. Their ERPs were recorded during an active auditory discrimination task of classic oddball paradigm. And then they were assessed by neuropsychological tests including minimental status examination(MMSE) and the Montreal cognitive assessment (MoCA). Result:Compared with the controls,the latencies of N2 complex and N2b at FZ site were significantly longer in SIVD group (P0.05),the scores of MMSE and MoCA slightly decreased but without significance. Conclusion:It's possibly seemed that the injury of SIVD mainly reduced the latency of N2 complex. So the latency of N2 complex and N2b might be chosen as an objective measurement in assessing the cognitive function of aged patients with SIVD.

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)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.013
GPT teacher head0.247
Teacher spread0.233 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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