Acetylcholinesterase inhibitors for treating dementia symptoms - a safety evaluation
Bibliographic record
Abstract
INTRODUCTION: The prevalence of Alzheimer's disease (AD) continues to rise, while treatment options for cognitive impairment are limited. Acetylcholinesterase inhibitors (AChEIs) aim to provide symptomatic benefit for cognitive decline, however these drugs are not without adverse events (AEs). The safety profile of each drug must be taken carefully into consideration before being prescribed, as new dosages and formulations have recently been approved. Areas covered: Donepezil, galantamine and rivastigmine are the three AChEIs approved for the treatment of varying stages of AD. Numerous clinical trials and post-marketing studies have evaluated the safety of these medications. This article will review the safety, efficacy and tolerability of these drugs in treating AD. Topics including pharmacovigilance databases, concomitant drug interactions, prescribing cascades, and treatment discontinuation are also covered. Expert opinion: AChEI use in those with mild, moderate or severe AD provide modest improvements in cognition, function and behavior. The pharmacological treatment of AD using AChEIs is associated with generally mild AEs. Differences in drug formulations should be taken into account when determining the most appropriate route of administration for each individual. Furthermore, discontinuation of AChEIs must be carefully monitored as it may be associated with worsening cognitive impairment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".