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Record W2803325855 · doi:10.1093/geronb/gby058

Variability and Coupling of Olfactory Identification and Episodic Memory in Older Adults

2018· article· en· W2803325855 on OpenAlexaff
Jamie Knight, David A. Bennett, Andrea M. Piccinin

Bibliographic record

VenueThe Journals of Gerontology Series B · 2018
Typearticle
Languageen
FieldNeuroscience
TopicOlfactory and Sensory Function Studies
Canadian institutionsUniversity of Victoria
FundersNational Institute on AgingNational Institutes of Health
KeywordsEpisodic memoryIdentification (biology)PsychologyCoupling (piping)Cognitive psychologyOlfactory perceptionOlfactionNeuroscienceComputer scienceBiologyCognitionMaterials science

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine whether assessment-to-assessment fluctuations in episodic memory (EM) reflect fluctuations in olfaction over time. METHODS: Within-person coupled variation in EM and the Brief Smell Identification Test (BSIT) was examined in 565 participants aged 58-106 with autopsy data from the Rush Memory and Aging Project. A growth model for up to 15 years of EM data, with BSIT as time-varying covariate, was estimated accounting for main effects of sex, education, ε4 allele, and Alzheimer's disease (AD) pathology, BSIT and time-varying BSIT, as well as the interaction between AD pathology and time-varying BSIT. RESULTS: Individuals with higher BSIT scores (b = .01, standard error [SE] = .004, p = .009) had slower declines in EM. High AD pathology (b = -.06, SE = .02, p = .001) was associated with more rapid declines in EM. The association between time-specific fluctuations in EM and BSIT differed by level of AD pathology (b = .08, SE = .034, p = .028), with a higher EM-BSIT association at higher levels of pathology. DISCUSSION: BSIT and EM fluctuate together over measurement occasions, particularly for individuals with AD pathology. Repeated intraindividual measurements provide information that could lead to early detection and inexpensive monitoring of accumulating AD pathology.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.520

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.121
GPT teacher head0.306
Teacher spread0.185 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueThe Journals of Gerontology Series BSame topicOlfactory and Sensory Function StudiesFrench-language works237,207