NEW EVIDENCE OF HEALTHIER AGING. POSITIVE COHORT EFFECT ON COGNITIVE DECLINE
Bibliographic record
Abstract
Abstract Cross sectional studies have shown cohort effects in cognition, limited research exists about cohort effects on cognitive trajectories. Indeed, most longitudinal research conducted to study aging-related cognitive change focus on the association between risk factors and mean change in cognition, considering individual differences too, but longitudinal norms of cognitive function are less studied. In this study, we aim to test whether cohort effects exist across the distribution of verbal fluency trajectories, that is, whether cohort effects vary across different trajectory quantiles. With this purpose, we estimated norms using data from 9 waves of the English Longitudinal Study of Aging (ELSA). We considered the individuals born in the 1920s, 1930s, and 1940s to assess cohort effects. The methodological framework consisted of quantile mixed models where the effect of age was adjusted using splines. To test for possible cohort effects across the 5th, 50th and 90th quartiles, the coefficients associated with the splines varied among cohorts. Our results suggest that cognitive decline is less pronounced for individuals born in more recent decades (p < 0.001), supporting our hypothesis of cohort effects. Moreover, these results are consistent across quantiles (p-value < 0.001). Additionally, we found that quantiles of verbal fluency at a certain age is higher in participants from more recent cohorts compared to those in older cohorts. Our findings contribute to a better understanding of cognitive decline in older adults, demonstrating population changes over time at different levels of changes in verbal fluency.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".