The benefits and risks associated with cholinesterase inhibitor therapy in Alzheimer’s disease
Bibliographic record
Abstract
The 'second-generation' cholinesterase inhibitors (ChEIs), donepezil, galantamine and rivastigmine, are a class of medications that are currently approved for the treatment of mild-to-moderate Alzheimer's disease (AD). These medications have proven efficacy in improving cognition, behaviour, activities of daily living, and global functioning in mild-to-moderate AD. They have also been shown to reduce caregiver stress and to delay time to nursing home placement. Two separate meta-analyses have indicated that ChEIs confer a modest but significant therapeutic benefit in the treatment of AD, despite higher rates of treatment discontinuation and side effects than placebo. There is growing evidence to support their efficacy in treating moderate-to-severe AD. ChEIs are generally well-tolerated, with side effects that tend to be dose-related and are most problematic during dose titration. The most common adverse effects, related to cholinergic stimulation in the brain and peripheral tissues, include gastrointestinal, cardiorespiratory, extrapyramidal, genitourinary, and musculoskeletal symptoms, as well as sleep disturbances. Few clinically significant drug-drug interactions with ChEIs have been identified. Three head-to-head trials of ChEIs in the treatment of AD have been published to date, but are limited due to their open-label design, rates of titration, and the drug dosage levels utilised. Further study is needed to examine other indications for ChEIs, as well as their combination with newer treatments, such as memantine.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".