Expensive Drugs for Rare Diseases in Canada: What Value and At What Cost?
Bibliographic record
Abstract
There has been explosive growth in the market for expensive drugs for rare diseases (EDRDs). Traditional standards of evidence are not achievable for rare diseases, so lower standards are applied. The price of these drugs is extremely high. This combination of lower standards and higher prices make EDRDs attractive to manufacturers. Legislation designed to incentivize drug development for rare diseases contains loopholes that drive prices up worldwide. Canada compounds those problems with a complex network of agencies that impede communication between those providing market authorization and those purchasing drugs. Drug pricing is not related to metrics like investment or value, but rather willingness to pay. Without high-quality evidence to assess value, we inadvertently prioritize patients with rare diseases over those with common diseases, creating conflict among ethical principles such as social utility, justice and the rule of rescue. Lack of transparency over what is being funded and for whom makes it hard to mitigate challenges through effective policy development. We review the evidentiary, economic and ethical issues around EDRDs and ways to move forward, including enhanced transparency and the development of high-quality evidence to ensure that we do not pay for drugs that do not work.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".