Aging and the Shape of Cognitive Change Before Death: Terminal Decline Or Terminal Drop?
Bibliographic record
Abstract
OBJECTIVES: Relative to typical age-related cognitive decrements, the terms "terminal decline" and "terminal drop" refer to the phenomenon of increased cognitive decline in proximity to death. Given that these terms are not necessarily synonymous, we examined the important theoretical distinction between the two alternative trajectories or shapes of changes they imply. METHODS: We used 12-year (5-wave) data from the Victoria Longitudinal Study to directly test whether pre-death cognitive decrements follow a terminal decline (generally gradual) or a terminal drop (more abrupt) shape. Pre-death trajectories of cognitive decline for n=265 decedents (Mage = 72.67 years, SD = 6.44) were examined separately for 5 key cognitive constructs (verbal speed, working memory, episodic memory, semantic memory, and crystallized ability). RESULTS: Several classes of linear mixed models evaluated whether cognitive decline increased per additional year closer to death. Findings indicated that the shape of pre-death cognitive change was predominantly characterized by decline that is steeper as compared with typical aging-related change, but still best described as slow and steady decline, especially as compared with precipitous drop. DISCUSSION: The present findings suggest that terminal decline and terminal drop trajectories may not be mutually exclusive but could rather reflect distinct developmental trajectories within the same individual.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".