Lessons learned from the Canadian Fabry Disease Initiative for future risk-sharing and managed access agreements for pharmaceutical and advanced therapies in Canada
Bibliographic record
Abstract
Risk sharing agreements (RSAs) and managed access agreements have emerged as tools to overcome evidentiary uncertainty and contain costs of pharmaceuticals; however, Canada has relatively little experience with these health policy instruments. This article describes one of the few examples of national RSAs. Enzyme replacement therapies (ERT) were introduced in Canada to treat Fabry disease in the early 2000s through an RSA. Based on qualitative interviews with key participating actors, this article explains how this RSA ensured continuity of treatment for patients already on ERT, and collected robust real-world evidence to secure treatment for future Fabry patients. We show the importance of partnerships, collaborations, and active patient communities in establishing RSAs, as well as the critical role of robust registries for the collection, storage, and use of that real-world data. In doing so, this paper points to reasons that explain the relative dearth of RSAs in Canada, which can be resource (both human and finance) intensive and are difficult to broker in a federalist health system. Through these findings, policy lessons are developed concerning the need for technological and governance platforms on how RSA in Canada can be more effectively supported going forward in a broader move towards "social pharmaceutical innovation".
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".