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Record W2731847102 · doi:10.1093/geroni/igx004.245

EARLY-LIFE AND LATE-LIFE COGNITIVE LIFESTYLE AS A WAY TO PROMOTE COGNITIVE RESERVE IN OLDER ADULTS

2017· article· en· W2731847102 on OpenAlexaff
Sylvie Belleville, A. Cordière, Gabriel Ducharme‐Laliberté, Benjamin Boller

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsInstitut Universitaire de Gériatrie de Montréal
Fundersnot available
KeywordsCognitive reserveCognitionPsychologyPsychological interventionBrain sizeCognitive declinePsychological resilienceGerontologyDevelopmental psychologyCognitive impairmentMedicineDementiaNeurosciencePsychiatrySocial psychologyDisease

Abstract

fetched live from OpenAlex

The reserve hypothesis suggests that some individuals develop a form of resilience against the detrimental effects of brain damage. Inter-individual differences in reserve have been related to a range of differences in cognitive lifestyle. This presentation will examine the evidence suggesting that differences in early-life education and late-life engagement in mentally stimulating leisure activities determine differences in baseline cognition and age-related cognitive decline. It will also assess the effects of early-life education and mentally stimulating leisure activities in late life on critical brain parameters, including brain volume, cortical thickness, and task-related activation. Based on these findings, this talk will discuss the potential for leisure-based interventions and present evidence of the impact of those interventions on cognitive and brain function in healthy older adults. It will conclude by presenting ENGAGE, a currently held project developed to increase reserve with enriched leisure activities.

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)0.005
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 score0.076
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.376
Teacher spread0.340 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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