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Family cognitive training for patients with vascular cognitive impairment

2014· article· en· W3029563036 on OpenAlexaboutno aff
Junqing Zhao, Qiu Xiaohong, Yuan Xue, Lifang Che, Liyun Guo

Bibliographic record

VenueZhonghua wuli yixue zazhi · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionMontreal Cognitive AssessmentCognitive trainingDementiaCognitive impairmentVascular dementiaPhysical therapyCognitive rehabilitation therapyPsychologyMedicineRehabilitationPhysical medicine and rehabilitationClinical psychologyPsychiatryDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Objective To observe the effects of family cognitive training on patients with vascular cognitive impairment but without dementia.Methods Sixty patients with non-dementia type vascular cognitive impairment were divided at random into a group which received family cognitive training (30 cases) and a control group (30 cases).The 2 groups all took routine drugs and exercise.The family cognitive training group received cognitive training additionally.Before treatment and after 1 and 6 months of treatment,all of the patients of both groups were assessed using the mini-mental state examination (MMSE),the Montreal cognitive assessment (MoCA) and the modified Barthel index (MBI).Results After 1 month of treatment there was no significant difference between the 2 groups in any of the assessments.After6 months the scores on each item of the MMSE,MoCA and MBI had improved significantly more in the family cognitive training group than in the control group.Conclusion Family cognitive training is effective in treating non-dementia type vascular cognitive impairment.It can delay disease progression and improve cognitive function and ability in the activities of daily living. Key words: Vascular cognitive impairment;  Family rehabilitation;  Cognitive training

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.033
GPT teacher head0.253
Teacher spread0.220 · 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.

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
Published2014
Admission routes1
Has abstractyes

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