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Record W2207263847 · doi:10.26021/7082

Mild cognitive impairment : improved identification and a novel cognitive intervention.

2015· article· en· W2207263847 on OpenAlexaboutno aff
Yan Chen Wang

Bibliographic record

VenueUniversity of Canterbury Research Repository (University of Canterbury) · 2015
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionCognitive impairmentIdentification (biology)Intervention (counseling)PsychologyCognitive psychologyComputer scienceMedicinePsychiatry

Abstract

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The prevention and treatment of cognitive impairment in the elderly has assumed increasing importance given the ageing population. Mild Cognitive Impairment (MCI) is considered as a transitional state between healthy ageing and dementia. The primary aim of the current study was to develop a cognitive intervention for MCI that encourages participation in a variety of complex and novel cognitive activities, and to examine its efficacy. Furthermore, these cognitive activities were developed to influence multiple brain networks, particularly the default mode network (DMN). A secondary aim was to assess the diagnostic utility of a number of screening measures in discriminating MCI. In the Cognitive Screening Study, 609 individuals (age range 65-97 years old) were evaluated using brief cognitive tests, including the Montreal Cognitive Assessment (MoCA), Rey Complex Figure Test (RCFT; copy and 3-min recall), and Trail Making Test-Part A (TMT-A). After the initial evaluation, 222 were excluded, the remaining were classified as Probable MCI (n = 75), Possible MCI (n = 72) and Probable Healthy Control (HC; n = 240). A portion of these individuals were followed-up with detailed cognitive assessment, and their performance on the detailed assessment determined which cognitive group they were assigned to. As a result, 17 individuals were classified as Confirmed MCI, 91 as Possible MCI, and 226 as Probable HC. The diagnostic utility of each individual screening measure was examined using the standard receiver operating characteristic curve (ROC). Multivariate logistic regression analysis was conducted to investigate whether combinations of the screening instruments improved the detection of MCI. Simultaneous discriminations among the three cognitive classes were examined using three-dimensional ROC. The results revealed that both MoCA and RCFT (copy and 3-min recall) demonstrated good discrimination of MCI, however, the combination of the two tests showed even better discriminatory power. Thirteen MCI participants were included in the Cognitive Enrichment Study, these individuals were randomly allocated to either the intervention (n = 6) or waitlist group (n = 7). Those in the intervention group received the 4-month-long Cognitive Enrichment Programme. Although the neuropsychological results were generally non-significant, we found a significant pre-post-effect on a measure of long-term memory retrieval. Furthermore, this study also used functional magnetic resonance imaging (fMRI) to examine the effect of enrichment on the DMN in MCI. DMN activity and connectivity were recorded pre- and post-enrichment. An increase in resting-state DMN connectivity was found in intervention participants, while the waitlist group showed a reduced connectivity. The changes in DMN connectivity were associated with an improvement on tests of executive function. However, there were no enrichment-related changes in DMN activation and deactivation. In conclusion, these results suggest some beneficial effects of cognitive enrichment on cognitive abilities, as well as DMN connectivity. Results from the current study, however, should be interpreted with caution because of the small sample size. Further larger trials are needed to confirm the preliminary findings of this study.

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)0.001
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.308
Teacher spread0.263 · 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
Published2015
Admission routes1
Has abstractyes

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