Bibliographic record
Abstract
BACKGROUND: Drug reimportation is perceived as a costs-cutting strategy by Americans. Nonetheless, issues such as drug safety and efficacy prevent legalization of the practice. With the contradictory views from supporters and opponents, debate on drug reimportation continues to snowball. The objective of this commentary is to discuss issues regarding drug reimportation practices in the United States (US). It also examines policy implications and potential solutions of the controversy. FINDINGS: Comparatively inexpensive drugs available across the border help Americans relieve the burden of medication costs. Consequently, the volume of reimported drugs entering the US has considerably increased. However, these practices are illegal and legalization of drug reimportation is a political debate. While safety is the most important barrier for legalization, this concern does not seem to affect growing number of Americans who are getting their prescriptions filled from across the border. Canadians oppose legalization of reimportation in the US as it could exacerbate the problem of medication shortage in Canada. SUMMARY: Currently, legalization of dug reimportation has wedged between the arguments by different groups. Until the US government finds a solution to reduce medication costs, it seems to be impossible to stop Americans from buying the comparatively inexpensive medications available across the border.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".