MétaCan
Menu
Back to cohort
Record W2899895238 · doi:10.1093/geroni/igy023.2909

DOES COGNITIVE LIFESTYLE MATTER? EXAMINING TRANSITIONS BETWEEN COGNITIVE STATES USING MSM ACROSS FOUR STUDIES

2018· article· en· W2899895238 on OpenAlexaff
Annie Robitaille, Ardo van den Hout, Emiel O. Hoogendijk, Andriy Koval, Judith J. M. Rijnhart, Johan Skoog, Scott M. Hofer

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsUniversity of VictoriaUniversité du Québec à Montréal
Fundersnot available
KeywordsCognitionPsychologyCognitive impairmentConsistency (knowledge bases)Cognitive declineLongitudinal studyGerontologyElderly peopleDevelopmental psychologyMedicinePsychiatryDementiaComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

We examined the relationship between active cognitive lifestyle (i.e. social network size and participation in cognitive activities) and transitions between different cognitive states (i.e., normal MMSE, mild MMSE impairment, severe MMSE impairment) and evaluated the consistency of results across four longitudinal studies of aging (i.e. OCTO-Twin, LASA, H70, MAP). Multi-state models (MSM) were run independently across all longitudinal studies controlling for age, sex, and education. Less participation in cognitive activities was associated with an increased risk of transitioning from normal MMSE to mild MMSE impairment and from normal MMSE to death in two studies. Larger social network size was associated with a lower risk of transitioning from normal MMSE to mild MMSE in three studies. These results highlight how lifestyles factors can delay cognitive decline and highlights the need for more policies and interventions that make it easier for older adults to keep their mind active and remain socially involved.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.119
GPT teacher head0.443
Teacher spread0.324 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInnovation in AgingSame topicHealth and Well-being StudiesFrench-language works237,207