Switching cholinesterase inhibitors in older adults with dementia
Bibliographic record
Abstract
BACKGROUND: Cholinesterase inhibitors (ChEIs) represent the mainstay of symptomatic treatment in Alzheimer's disease. Three medications belonging to this class are presently widely available. These agents differ in their individual mechanisms of action and pharmacokinetic properties. Switching ChEIs can be a reasonable option in cases of intolerance or lack of clinical benefit. METHODS: A systematic literature search of switching ChEIs was conducted, and all studies specifically evaluating this issue were identified. Published consensus guidelines were also searched for recommendations on ChEI switching. RESULTS: Eight clinical studies are summarized and discussed. All of these studies are open-label or retrospective and they cannot be readily compared because of heterogeneity in design, number of patients, agents used, and endpoints. Switching in most of these studies was done for both "lack of benefit" or "loss of response" after up to 29 months of treatment. Nevertheless, the majority of studies did not include individuals switched for lack of response after several years of treatment. Lack of satisfactory response or intolerance with the initial agent was not predictive of similar results with the second agent. CONCLUSIONS: In light of these findings, we propose the following practical approach to switching ChEIs: (1) in the case of intolerance, switching to a second agent should be done only after the complete resolution of side-effects following discontinuation of the initial agent; (2) in the case of lack of efficacy, switching can be done overnight, with a quicker titration scheme thereafter; (3) switching ChEIs is not recommended in individuals who show loss of benefit several years after initiation of treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".