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Record W2376772779

The Research of the Patients with Vascular Cognitive Impairment no Dementia in Terms of Neuropsychology

2007· article· en· W2376772779 on OpenAlexaboutno aff
WU Nan

Bibliographic record

VenueLiaoning Yixueyuan xuebao · 2007
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaNeuropsychologyPsychologyClinical Dementia RatingCognitionCognitive impairmentMontreal Cognitive AssessmentOrientation (vector space)Mini–Mental State ExaminationNeuropsychological testAudiologyClinical psychologyRating scaleVascular dementiaPhysical therapyPhysical medicine and rehabilitationMedicinePsychiatryDevelopmental psychologyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Objective This paper is to investigate the characteristics of the patients with Vascular Cognitive Impairment of None dementia(VCIND) in terms of neuropsychology.Methods 42 patients with VCIND and 38 normal controls were examined with the neuropsychological test,including:mini-mental state examination(MMSE),activities of daily living scale(ADL),Preffer outpatient disability questinnair(POD),clock drawing test(CDT)and clinical dementia rating scale(CDR),Hachinski ischaemic scale(HIS),and center for epidemiological studiesdepression scale(CES-D).Results The scores of MMSE,CDT in the VCIND group were significantly lower than those in the control group(P0.05);the scores in the VCIND group,as far as place orientation,time orientation,attention calculation,verbal recognition memory,writing ability and structure ability were concerned,were significantly lower than those in the normal control group(P0.05).Conclusions The combination of CDT and MMSE can be used for early screening of cognitive impairment.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.037
GPT teacher head0.317
Teacher spread0.280 · 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
Published2007
Admission routes1
Has abstractyes

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