Discontinuing cholinesterase inhibitors: results of a survey of Canadian dementia experts
Bibliographic record
Abstract
BACKGROUND: Cholinesterase inhibitors (ChEIs) are being used for increasingly long periods of time, even in patients with severe Alzheimer's disease. Because there is little data to help clinicians to decide on when it is safe and appropriate to discontinue ChEIs after long-term use, practices may vary widely. METHODS: An internet-based survey was undertaken of Canadian dementia experts (geriatric psychiatrists, neurologists, geriatricians) involved in clinical trial research. Recommendations for ChEI discontinuation were determined based on responses to questions dealing with patient/caregiver preference, administrative considerations, effectiveness, and adverse events. RESULTS: There was reasonable consensus that ChEIs should be discontinued based on patient and caregiver preference, and in the presence of severe bothersome adverse events. There was much less consensus on issues related to effectiveness - in particular, what constitutes greater than expected decline. There was a general reluctance to rely on any single measure of cognition, function and/or behavior, and in particular, the MMSE was seen as unhelpful for making decisions about discontinuation. CONCLUSION: Recommendations for discontinuing ChEIs after long-term use from a survey of dementia experts are presented. Ideally, clinical practice guidelines based on controlled discontinuation trials are needed.
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 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".