Update on the Pharmacological Treatment of Alzheimers Disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disorder. Worldwide prevalence of the disease is estimated at more than 24 million cases. With aging of populations, this number will likely increase to more than 80 million cases by the year 2040. The annual incidence worldwide is estimated at 4.6 million cases which is the equivalent of one new case every seven seconds! The pathophysiology of AD is complex and largely misunderstood. It is thought to start with the accumulation of beta-amyloid (αβ) that leads to deposition of insoluble neuritic or senile plaques. Secondary events in this "amyloid cascade" include hyperphosphorylation of the protein tau into neurofibrillary tangles, inflammation, oxidation, and excitotoxicity that eventually cause activation of apoptotis, cell death and neurotransmitter deficits. This review will briefly summarize recent advances in the pathophysiology of AD and focus on the pharmacological treatment of the cognitive and functional symptoms of AD. It will discuss the roles of vascular prevention, cholinesterase inhibitors and an NMDA-antagonist in the management of AD. It will address the issues thought to be related to the lack of persistence or discontinuation of therapy with cholinesterase inhibitors shown in recent studies and some of the solutions proposed. These include setting realistic expectations in light of a neurodegenerative condition and available symptomatic treatments, slowly titrating medications, and using alternate routes of administration. Finally, it will introduce future therapeutic options currently under study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".