A review of clinical treatment considerations of donepezil in severe Alzheimer's disease
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder that affects over 45 million people worldwide. Patients with severe AD require help with daily activities and show severe memory impairment. Currently, donepezil is one of two drugs approved by FDA and Health Canada for the treatment of severe AD (MMSE score <10). It is prescribed as 5 or 10 mg/d and an FDA-approved 23-mg/d dose. METHOD: This review will discuss risks and benefits of donepezil at these doses in severe AD. Articles were identified using PubMed using the MeSH terms "donepezil" AND "Alzheimer Disease" AND "severe." Three double-blind, placebo-controlled, randomized studies, one post hoc analysis, and one subgroup analysis were selected. RESULTS: Donepezil was found to benefit patients in cognition and global functioning. The most consistent improvement was in severe impairment battery (SIB) scores. However, more patients treated with high dosage of donepezil discontinued their treatment due to various adverse events (AEs). CONCLUSION: Clinicians must weigh benefits against adverse events when determining the course of therapy, as recommendations for cholinesterase inhibitors in advanced AD remain unclear and vary with different guidelines.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".