CCCDTD5 recommendations on the deprescribing of cognitive enhancers in dementia
Bibliographic record
Abstract
INTRODUCTION: Cognitive enhancers (ie, cholinesterase inhibitors and memantine) can provide symptomatic benefit for some individuals with dementia; however, there are circumstances in which the risks of continuing treatment may potentially outweigh benefits. The decision to deprescribe cognitive enhancers must consider each patient's preferences, treatment indications, current clinical status and symptoms, prognosis, and dementia type. METHODS: The 5th Canadian Consensus Conference on the Diagnosis and Treatment of Dementia (CCCDTD5) established a subcommittee of experts to review current evidence on the deprescribing of cognitive enhancers. The questions answered by this group included: When should cognitive enhancers be deprescribed in persons with dementia and mild cognitive impairment? How should cognitive enhancers be deprescribed? And, what clinical factors should be considered when deprescribing cognitive enhancers? RESULTS: Patient and care-partner preferences should be incorporated into all decisions to deprescribe cognitive enhancers. Cognitive enhancers should be discontinued in individuals without ongoing evidence of benefit or when the indication for cognitive enhancer use was inappropriate (eg, mild cognitive impairment). Deprescribing should occur gradually and cognitive enhancers should be reinitiated if patients' cognition or function deteriorates. Cognitive enhancers should be continued in individuals whose neuropsychiatric symptoms improve in response to treatment. Clinicians should not deprescribe cognitive enhancers in individuals with significant neuropsychiatric symptoms until symptoms have stabilized. CONCLUSION: CCCDTD5 deprescribing recommendations provide evidence-informed recommendations related to cognitive enhancer deprescribing that will facilitate shared decision making among patients, care partners, and clinicians.
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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 | 0.037 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.014 | 0.007 |
| Research integrity | 0.036 | 0.018 |
| Insufficient payload (model declined to judge) | 0.021 | 0.011 |
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".