The Effect of US Pharmaceutical Drug Importation on the Canadian Pharmaceutical Supply
Bibliographic record
Abstract
Background and objectives: For over a decade, many US politicians have advocated that the US allow personal and commercial drug importation. Currently, the only entities that can legally import a pharmaceutical in the US are pharmaceutical manufacturers. Our objective was to compare the number of prescriptions dispensed in Canada with the US and estimate the effect US drug importation from Canada will have on the Canadian drug supply. Methods: A model was created to measure the potential effect on the Canadian drug supply. The model uses the number of US prescriptions being sourced from Canada and the number of prescriptions dispensed in Canada in 2007 as the baseline. The number of days to exhaust the 2007 Canadian drug supply was calculated. Results: The model found that if 10% of the US prescriptions were filled from Canadian sources (manufacturer, wholesale or retail), Canada's 2007 drug supply would be exhausted in 224 days. If the demand from the US reached 20%, the 2007 supply would be exhausted in 155 days. The model was redone focusing on brand name drugs, with generic drugs removed. It was found that with a US demand of 10% and 20%, the 2007 Canadian supply for brand name drugs would be exhausted in 268 and 201 days, respectively. Conclusion: US drug importation is a threat to Canada's drug supply. Even if the US demand were 10%, Canada would need to dramatically increase manufacturing, triple drug importation, or most likely control or halt pharmaceutical shipments to the US.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".