Preclinical Cognitive Trajectories Differ for Alzheimer's Disease and Vascular Dementia
Bibliographic record
Abstract
We investigated differences between Alzheimer's disease (AD) and vascular dementia (VaD) from the appearance of the first cognitive symptoms, focusing on both time of onset and rate of accelerated decline for different cognitive functions before dementia diagnosis. Data from a longitudinal population-based study were used, including 914 participants (mean age = 82.0 years, SD = 5.0) tested with a cognitive battery (word recall and recognition, Block Design, category fluency, clock reading) on up to four occasions spanning 10 years. We fit a series of linear mixed effects models with a change point to the cognitive data, contrasting each dementia group to a control group. Significant age-related decline was observed for all five cognitive tasks. Relative to time of diagnosis, the preclinical AD persons deviated from the normal aging curve earlier (up to 9 years) compared to the preclinical VaD persons (up to 6 years). However, once the preclinical VaD persons started to decline, they deteriorated at a faster rate than the preclinical AD persons. The results have important implications for identifying the two dementia disorders at an early stage and for selecting cognitive tasks to evaluate treatment effects for persons at risk of developing AD and VaD.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".