Use of Lecanemab and Donanemab in the Canadian Healthcare System: Evidence, Challenges, and Areas for Future Research
Bibliographic record
Abstract
ABSTRACT Lecanemab and donanemab are monoclonal antibody therapies that remove amyloid-beta from the brain. They are the first therapies that alter a fundamental mechanism, amyloid-beta deposition, in Alzheimer disease (AD). To inform Canadian decisions on approval and use of these drugs, the Canadian Consortium on Neurodegeneration in Aging commissioned Work Groups to review evidence on the efficacy, and safety of these new therapies, as well as their projected impacts on Canadian dementia systems of care. We included persons with lived experience with Alzheimer disease in the discussion about the benefits and harms. Our review of the trial publications found strong support for statistically significant group differences, but also recognized that there are mixed views on the clinical relevance of the observed differences and the value of therapy for individual patients. The drugs are intended for persons with early AD, at a stage of mild cognitive impairment or mild dementia. If patients are treated, then confirmation of AD by positron emission tomography or cerebrospinal fluid analysis and monitoring for risk of amyloid-related imaging abnormalities was recommended, as done in the clinical trials, although it would strain Canadian resource capacity. More data are needed to determine the size of the potentially eligible treatment population in Canada.
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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 | 0.041 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".