A Model Predicting Healthcare Capacity Gaps For Alzheimer’s Disease-Modifying Treatment in Canada
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is experienced by > 600,000 Canadians. Disease-modifying therapies (DMTs) for earlier stages of disease are in development. Existing health system capacity constraints and the need for biomarker-driven diagnostics to confirm DMT eligibility are concerning. This study aimed to characterize the capacity gap related to early AD (eAD) treatment with DMTs in Canada. METHODS: A capacity model was developed to simulate the flow of a patient from screening to treatment for eAD to quantify the gap between available and required healthcare resources and qualify the bottlenecks restricting the patient journey at a provincial and national level. The model inputs (epidemiological, human resource, and clinical) were evidence-based, healthcare professional-, and patient advocate-informed. RESULTS: The model estimated that nationally < 2% of patients would have access to the required healthcare resources for treatment with a DMT. Eligibility assessment represented the step with the largest capacity gap across all provinces, with a wait list of about 382,000 Canadians one year following DMT introduction. The top three resource gaps included AD specialist time and positron emission tomography and magnetic resonance imaging exam slots. Sensitivity analysis showed that full reliance on cerebrospinal fluid for eligibility testing increased capacity for assessment by about 47,000 patients. CONCLUSION: This model highlights that the Canadian health system is critically under-resourced to diagnose, assess, and treat patients with eAD with DMT. It underscores an urgent need for national policy and provincial resource allocation to close the gap.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".