Can Managed Access Agreements Mitigate Evidentiary, Economic and Ethical Issues with Access to Expensive Drugs for Rare Diseases in the Canadian Context?
Bibliographic record
Abstract
Expensive drugs for rare diseases (EDRDs) pose challenges for regulatory and reimbursement decision makers. Managed access agreements (MAAs), conditional reimbursement schemes that use a variety of price and evidence generation mechanisms to support value-based decision making, have the potential to address the evidentiary, economic and ethical issues associated with EDRDs. Several jurisdictions have successfully used MAAs to manage budget impact and evidentiary uncertainties, demonstrating the promise of this approach. We comment on the feasibility of adopting MAAs in Canada to address challenges associated with EDRDs. Adopting MAAs in the Canadian context requires attention to Canada's federated healthcare and drug coverage system and will require investing in robust data infrastructure and governance systems.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.049 | 0.039 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".