Early Life Development in a Multiethnic Sample and the Relation to Late Life Cognition
Bibliographic record
Abstract
OBJECTIVES: Poor quality of early life conditions has been associated with poorer late life cognition and increased risk of dementia. Early life physical development can be captured using adult measures of height and head circumference. Availability of resources may be reflected by socioeconomic indicators, such as parental education and family size. We sought to determine the association between early life development and experience and late life semantic memory, episodic memory, and executive functioning abilities, as well as rate of cognitive decline. METHOD: This study was conducted using the UC Davis Aging Diversity cohort, an ethnically diverse sample of Caucasian, African American, and Hispanic individuals from northern California. We used latent variable modeling to measure growth and childhood socioeconomic environment (SES) and examine their associations with longitudinal cognitive outcomes using mixed effects modeling. RESULTS: Growth was positively related to higher childhood SES. Higher childhood SES was associated with better semantic memory. Both low growth and low SES were associated with increased rate of cognitive decline. DISCUSSION: These findings demonstrate that early life experiences influence the trajectory of cognitive aging. Early life development and experience appears to provide a distal basis upon which additional risk and protective factors interact in the development of dementia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".