Bibliographic record
Abstract
Abstract: Dementia is a progressive condition affecting millions globally, posing a significant public health challenge as populations age. Currently, over 55 million people live with dementia, including subtypes such as Alzheimer’s disease, Lewy body dementia, vascular dementia, frontotemporal dementia, and HIV-associated dementia. This review explores advancements in diagnosing, managing, and preventing dementia. Diagnostic accuracy has been enhanced with tools like the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), cerebro-spinal fluid biomarkers, and PET imaging. Emerging technologies, including artificial intelligence and digital tools, promise earlier detection. Management strategies integrate non-pharmacologic approaches—cognitive training, lifestyle modifications, and social engagement—with pharmacologic treatments such as cholinesterase inhibitors and memantine. Prevention efforts emphasize addressing modifiable risk factors, such as hypertension and obesity, and fostering cognitive reserve through education and physical activity. Despite these advancements, challenges persist, including ethical concerns surrounding early diagnosis, disparities in access to care, and ongoing debates regarding the efficacy of novel therapies. A holistic, interdisciplinary approach is essential for effective dementia care. This review calls for collaborative action among healthcare professionals, policymakers, and researchers, emphasizing the urgent need to improve equitable access to care, promote early and accurate diagnosis, and invest in targeted prevention strategies to mitigate the growing global impact of dementia.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".