Memory and Cognition in Preclinical Dementia: What We Know and What We Do Not Know
Bibliographic record
Abstract
This overview focuses on the cognitive transition between normal aging and dementia. Numerous studies indicate that individuals who will go on to develop dementia show cognitive deficits many years before the time at which a clinical diagnosis could be rendered. The degree of preclinical impairment is remarkably similar for tasks assessing episodic memory, executive functioning, and perceptual speed, consistent with the view that multiple brain alterations occur prior to clinical disease onset. Although most research in this area has dealt with Alzheimer disease (AD), several recent reports indicate that the pattern of preclinical impairment is very similar in the second largest dementia disorder, vascular dementia (VaD). This is important because currently the possibility for interventions to postpone disease onset is greater in VaD than in AD. Despite pronounced preclinical cognitive deficits in dementia, the performance distributions between cases and controls are largely overlapping, hampering the ability to identify high-risk individuals. To alleviate this problem, future research should evaluate hybrid models for the prediction of dementia. In such models, multiple indicators of cognitive functioning should be included along with markers from other domains that have been linked to subsequent dementia (such as brain imaging, genetics, and lifestyle variables). To the extent that these categories of variables add unique variance, classification accuracy will increase and the overlap in performance scores between incident cases and controls will decrease, thereby enhancing clinical usefulness. This approach would also facilitate the examination of interactive effects among classes of preclinical markers.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".