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The effects of cognitive training on mild-to-moderate Alzheimer's disease patients

2011· article· en· W3029887079 on OpenAlexaboutno aff
牛轶瑄, 谭纪平, 管锦群, Zengqiang Zhang, 王晓红, 王鲁宁

Bibliographic record

VenueZhonghua wuli yixue zazhi · 2011
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMemory spanNeuropsychiatryCognitionRandomized controlled trialMedicineMontreal Cognitive AssessmentPhysical therapyPsychologyInternal medicineCognitive impairmentPsychiatryWorking memory

Abstract

fetched live from OpenAlex

Objective To determine the efficacy of individual cognitive training (CT) in the treatment of cognitive and neuropsychiatric symptoms in patients with mild to moderate Alzheimer disease ( AD). Methods A randomized, controlled, rater-blind clinical trial recruited 32 AD patients. AH patients were assigned to a CT group (n = 16) or a control group (a time and attention control, n = 16) for 10 weeks. All outcome measures were administered at baseline and follow-up. The cognitive status was evaluated using the Mini Mental State examination (MMSE) , a clock-drawing test (CDT) , Fuld's object memory evaluation (FOME) , a rapid verbal retrieval (RVR) , digit span assessments (DS) , block designing (BD), and the A version of the trail making test (TMTA). The patients' functional status was evaluated using an activities of daily living (ADL) scale. Any psychological and behavioural disorders were evaluated with the Neuropsychiatry Inventory ( NPI). Results Patients receiving CT showed greater average improvements in NPI total score, TMT-A score and MMSE total score than the controis at week 10. There was no statistically significant benefit for CT-treated patients in terms of ADL score. Conclusions Cognitive training can raise the NPI total scores and MMSE total scores of patients with mild to moderate AD. Key words: Alzheimer's disease;  Cognitive training;  Randomized controlled trials

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.647

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 head0.045
GPT teacher head0.311
Teacher spread0.266 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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