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Record W2347282457

Characteristics of MoCA test in the mild cognitive impairment patients aged over 80 years with office backgrounds

2011· article· en· W2347282457 on OpenAlexaboutno aff
Shuai Su-ron, FU Xiao-s

Bibliographic record

VenueShiyong laonian yixue · 2011
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentMedicineCognitive impairmentCognitionExecutive functionsPhysical therapyGerontologyInternal medicinePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

Objective To study the characteristics of Montreal cognitive assessment(MoCA) scale in the mild cognitive impairment(MCI) patients aged over 80 years with office backgrounds. Methods Eighty-three cases aged over 65 years with office backgrounds in Nanjing were selected and diagnosed by clinic parameters and the MoCA scale.The cases were divided into three groups: patients aged under 80 years with MCI(group A),patients aged over 80 years with MCI(group B),patients aged over 80 years without MCI(group C).The scores of MoCA scale in three groups were compared. Results The scores of the executive function and word fluency were worse in group B than those in group A and group C.The levels of serum cholesterol and platelet aggregation rate in group A were highest among the three groups. Conclusions The cognitive functions,especially executive function,are decreased with aging in older MCI patients because of atherosclerosis and so on.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.274
Teacher spread0.252 · 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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Same venueShiyong laonian yixueSame topicDementia and Cognitive Impairment ResearchFrench-language works237,207