Regulatory approval and public drug plan listing of new drugs for rare disorders in Canada and New Zealand
Bibliographic record
Abstract
A previous assessment of the alignment of health technology assessments and price negotiations for new drugs for rare disorders in Canada completed between 2014 and 2018 demonstrated that it is working for governments but has yet to lead to improved access in a timely manner for all appropriate patients in all provinces. In this analysis, drugs for rare and ultra-rare disorders with a completed price negotiation or no negotiation between 2014 and 2018 in Canada, and their reimbursement recommendations and listings in Canadian public drug programs are compared with their regulatory approval in New Zealand and listing in the New Zealand National Formulary. The results show that pharmaceutical manufacturers generally seek regulatory approval for rare disorder drugs in Canada before New Zealand, and fewer rare disorder medicines receive regulatory approval in New Zealand. One reason for this difference might be New Zealand's smaller population. However, another reason is likely the restrictive drug formulary in New Zealand. Drugs not given coverage in New Zealand are frequently made unavailable by the manufacturer. Planned changes to Canada's pricing regulations and guidelines will significantly diminish the country's attractiveness as a place in which pharmaceutical companies want to do business, which has the potential to negatively impact the health of all Canadians irrespective of whether they have private or public drug coverage.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".