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Record W2145318223 · doi:10.1037/neu0000150

ApoE and pulse pressure interactively influence level and change in the aging of episodic memory: Protective effects among ε2 carriers.

2014· article· en· W2145318223 on OpenAlexafffund
G. Peggy McFall, Sandra A. Wiebe, David Vergote, David Westaway, Jack H. Jhamandas, Lars Bäckman, Roger A. Dixon

Bibliographic record

VenueNeuropsychology · 2014
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
FundersNational Institute on AgingVetenskapsrådetNational Institutes of HealthSwedish Brain PowerStichting af Jochnick FoundationAlberta Health Services
KeywordsApolipoprotein EPsychologyEpisodic memoryEffects of sleep deprivation on cognitive performanceCognitionDevelopmental psychologyGerontologyInternal medicineMedicineNeuroscience

Abstract

fetched live from OpenAlex

OBJECTIVE: We tested independent and interactive effects of Apolipoprotein E (ApoE) and pulse pressure (PP) concurrently and longitudinally across 9 years (3 waves) of episodic (EM) and semantic memory (SM) data from the Victoria Longitudinal Study. METHOD: We assembled a sample of older adults (n = 570, baseline M age = 71, age range = 53-95) and used latent growth modeling to test 4 research goals. RESULTS: First, the best fitting memory model was 2 single latent variables for EM and SM, each exhibiting configural, metric, and partial scalar invariance. This model was analyzed as a parallel process model. Second, baseline level of PP predicted EM performance at centering age (75) and rate of 9-year EM change. Third, we observed no main effects of ApoE on EM or SM. Fourth, EM was affected by higher PP but differentially less so for carriers of the ApoE ε2 allele than the ε3 or ε4 alleles. CONCLUSIONS: PP is confirmed as a risk factor for concurrent and changing cognitive health in aging, but the effects operate differently across risk and protective allelic distribution of the ApoE gene.

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 score0.132
Threshold uncertainty score0.349

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 head0.024
GPT teacher head0.331
Teacher spread0.306 · 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

Citations30
Published2014
Admission routes2
Has abstractyes

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