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Record W4402543856 · doi:10.1093/geroni/igae082

New Evidence of Healthier Aging: Positive Cohort Effects on Verbal Fluency

2024· article· en· W4402543856 on OpenAlexaff
Fernando Massa, Alejandra Marroig, Joseph Lee Rodgers, Scott M Hoffer, Graciela Muñiz‐Terrera

Bibliographic record

VenueInnovation in Aging · 2024
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsUniversity of Victoria
FundersNational Institute on AgingNational Institutes of Health
KeywordsVerbal fluency testFluencyPsychologyCohortCognitive psychologyDevelopmental psychologyGerontologyMedicineCognitionNeuroscienceNeuropsychologyInternal medicineMathematics education

Abstract

fetched live from OpenAlex

Background and Objectives: Cross-sectional studies have shown improvements in cognition in later-born cohorts. However, it remains unclear whether these cohort effects extend beyond cognitive levels and are also detectable in the rate of age-related cognitive decline. Additionally, evidence is scarce on the presence and consistency of cohort effects throughout different segments of the distribution of cognitive trajectories. Research Design and Methods: This study evaluates the existence and variability of cohort effects across the entire distribution of aging-related trajectories of verbal fluency. With this purpose, we develop sex and education-adjusted longitudinal norms of verbal fluency using data from 9 waves of the English Longitudinal Study of Aging (ELSA) by fitting quantile mixed models. The effect of age was modeled using splines to assess birth cohort effects, after grouping individuals in 5-year groups from 1920 to 1950 according to their age at study entry. To test for possible cohort effects across the 10th, 50th, and 90th quantiles, the coefficients associated with the splines were allowed to vary among cohorts. Results: < .001), supporting the hypothesis of cohort effects. Additionally, we also found that quantiles of verbal fluency at any age are shifted upwards in later-born cohorts compared to those in earlier-born cohorts. Discussion and Implications: These results enhance our understanding of cognitive decline in older adults by demonstrating that cohort effects on cognition are observable both cross-sectionally and longitudinally, affecting the entire range of verbal fluency trajectories.

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 score0.245
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.026
GPT teacher head0.386
Teacher spread0.360 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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