Tracking Cognition-Health Changes From 55 to 95 Years of Age
Bibliographic record
Abstract
OBJECTIVES: Among the key targets of inquiry in cognitive aging are (1) the description of cognitive changes with advancing age and (2) the association of such cognitive changes with modulating factors in the changing epidemiological context. METHODS: In the current study, we assemble multi-occasion (up to 12 years) cognitive (speed, episodic memory, and semantic memory) and self-reported health data from the Victoria Longitudinal Study (n = 988; ages 55-95 years). RESULTS: The results from piecewise random effects models using age as a basis indicated that only selected measures of episodic memory and semantic memory showed evidence of significant declines prior to age 75. After age 75, all cognitive abilities showed evidence for statistically significant declines, although the magnitude of these changes varied considerably. Performance at age 75 was correlated with self-reported health for measures of processing speed and episodic memory. Changes in health status were related to changes in some aspects of processing speed. DISCUSSIONS: The results indicated that (1) for many cognitive abilities declines in performance did not manifest until after age 75 and (2) self-reported health was related to level of performance more than changes over age.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".