Blame Canada (and the Rest of the World): The Twenty-Year War on Imported Prescription Drugs
Bibliographic record
Abstract
Rising budget deficits and sticker shock over the new Medicare drug benefit have put the issue of prescription drug costs back into the spotlight. The growth in the cost of prescription drugs continues to represent a staggering burden for taxpayer-funded health care programs, even while costs of non-drug health care services have slowed or even decreased. Among the many proposals for cutting prescription drug costs, drug importation is unique. Although bipartisan support for drug importation has existed in Congress for over five years, the federal government continues to maintain that a system of safe and effective drug importation is impossible. This paper provides a comprehensive analysis of importation law and legislation as it has evolved over the past twenty years. The paper tells the story of drug importation’s checkered legislative history, beginning with adulterated Guatemalan birth-control pills and culminating with an unprecedented trade embargo by Canadian officials, which may soon prohibit all drug sales to customers in the United States. Additionally, the paper looks at the case law that has arisen from drug importation and describes how consumers and state governments are now turning to the judicial branch to force the federal government to ensure that imported prescription drugs are safe and effective for consumers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".