Strategies for continued successful treatment of Alzheimer's disease: switching cholinesterase inhibitors
Bibliographic record
Abstract
Cholinesterase (ChE) inhibitors represent the standard therapeutic approach to the treatment of Alzheimer's disease (AD). However, a proportion of patients experience lack or loss of therapeutic benefit with an initial agent, or discontinue due to safety/tolerability issues. In many instances, no alternative treatment is offered once the initial agent has been stopped. Thus, for many patients, the total duration of treatment is relatively short in comparison with the chronic nature of AD. Switching medications is a common therapeutic strategy within many drug classes across many clinical areas following a lack/loss of efficacy or safety/tolerability problems, and is also an increasingly important concept in the management of AD with ChE inhibitors. A number of open-label studies, where patients were switched from donepezil to rivastigmine, have indicated that approximately 50% of patients experiencing a lack/loss of efficacy with donepezil (a selective acetylcholinesterase [AChE] inhibitor) respond to subsequent treatment with rivastigmine (a dual AChE and butyrylcholinesterase inhibitor). In these studies, rivastigmine was well tolerated, and the occurrence of safety/tolerability problems with donepezil was not predictive of similar problems with rivastigmine. In the summer of 2002, leading neurologists and psychiatrists attended a medical experts meeting to discuss the clinical importance of switching ChE inhibitors in AD. The expert panel examined available clinical data, shared clinical experiences, and discussed current clinical guidelines for switching. The panel also aimed to reach consensus on 'whom to switch', 'when to switch' and 'how to switch'. The key findings from that meeting are reported in this review.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".