Health System Change for Alzheimer’s Disease-Modifying Therapies in Canada: Beginning the Discussion
Bibliographic record
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for 60%-70% of patients with dementia, and it is estimated that over one million Canadians will be living with dementia by 2030. Disease-modifying therapies (DMTs) targeting the underlying pathophysiology of AD are currently in development. Several models have demonstrated that the potential arrival of Alzheimer's DMTs will most likely overwhelm the already-constrained Canadian healthcare system. Canada does not have a strategy to address the extensive requirements of using DMTs, including providing an early diagnosis of AD, confirming DMT eligibility via amyloid biomarkers, and conducting ongoing treatment monitoring. Thus, a multidisciplinary group of experts involved in AD care in Canada gathered to review (1) the current barriers to diagnosis and management of AD; (2) how existing clinic models, including those used in multiple sclerosis (MS), could be applied to address key barriers in AD; and (3) how to design and implement optimal care pathways in the future. The actions outlined in this review will help clinicians and healthcare systems improve readiness to integrate the use of disease-modifying therapies in Alzheimer's disease, if such therapies are approved 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".