Terminal Decline From Within- and Between-Person Perspectives, Accounting for Incident Dementia
Bibliographic record
Abstract
OBJECTIVE: The terminal cognitive decline hypothesis has been debated for almost 50 years. This hypothesis implies a change in rate of decline within an individual. Therefore, we examine the hypothesis from a within-person perspective using a time to death chronological structure. METHOD: Scores on a Swedish version of the Wechsler Adult Intelligence Scale Information and Block Design scores from 461 OCTO-Twin Study participants with confirmed death dates were modeled using quadratic growth curve models including both age and distance from death at study entry, sex, education, and dementia diagnosis as covariates of initial performance and of linear and quadratic change over time. RESULTS: Information scores showed statistically significant evidence of slight within-person acceleration of declines in the no dementia group. Individuals with incident dementia declined more quickly, and those who were closer to death at study baseline had a stronger acceleration. Block Design scores declined but did not show evidence of such acceleration either within or across individuals. Decline was faster in incident cases closer to death at study entry. DISCUSSION: Within-person evidence of terminal decline is not as strong as previously published between-person results. Strategies for focusing models on longitudinal aspects of available data and the extent to which lack of within-person evidence for terminal decline may stem from common data limitations are discussed.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".