The Influence of Birth Cohorts on Future Cognitive Decline
Bibliographic record
Abstract
BACKGROUND: Slowed rates of cognitive decline have been reported in individuals with higher cognitive reserve (CR), but interindividual discrepancies remain unexplained. Few studies have reported a birth cohort effect, favoring later-born individuals, but these studies remain scarce. OBJECTIVE: We aimed to predict cognitive decline in older adults using birth cohorts and CR. METHODS: Within the Alzheimer's Disease Neuroimaging Initiative, 1,041 dementia-free participants were assessed on four cognitive domains (verbal episodic memory; language and semantic memory; attention; executive functions) at each follow-up visit up to 14 years. Four birth cohorts were formed according to the major historical events of the 20th century (1916-1928; 1929-1938; 1939-1945; 1946-1962). CR was operationalized by merging education, complexity of occupation, and verbal IQ. We used linear mixed-effect models to evaluate the effects of CR and birth cohorts on rate of performance change over time. Age at baseline, baseline structural brain health (total brain and total white matter hyperintensities volumes), and baseline vascular risk factors burden were used as covariates. RESULTS: CR was only associated with slower decline in verbal episodic memory. However, more recent birth cohorts predicted slower annual cognitive decline in all domains, except for executive functions. This effect increased as the birth cohort became more recent. CONCLUSION: We found that both CR and birth cohorts influence future cognitive decline, which has strong public policy implications.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".