Bibliographic record
Abstract
OBJECTIVE: Many advances have been made in the understanding of age-related changes in cognition. As research details the cognitive and neurobiological changes that occur in aging, there is increased interest in developing and understanding methods to prevent, slow, or reverse the cognitive decline that may occur in normal healthy older adults. The Institute of Medicine has recently recognized cognitive aging as having important financial and public health implications for society with the increasing older adult population worldwide. Cognitive aging is not dementia and does not result in the loss of neurons but rather changes in neurotransmission that affect brain functioning. The fact that neurons are structurally intact but may be functionally affected by increased age implies that there is potential for remediation. METHOD AND RESULTS: This review article presents recent work using medication-based strategies for slowing cognitive changes in aging. The primary method presented is a hormonal approach for affecting cognition in older women. In addition, a summary of the work examining modifiable lifestyle factors that have shown promise in benefiting cognition in both older men and women is described. CONCLUSIONS: Much work remains to be done so that evidence-based recommendations can be made for slowing cognitive decline in healthy older adults. The success of some of these methods thus far indicates that the brains of healthy older adults are plastic enough to be able to respond to these cognitive decline prevention strategies, and further work is needed to define the most beneficial methods.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".