Investigating pharmaceutical marketing in Canada using American prosecutions
Bibliographic record
Abstract
BACKGROUND: Pharmaceutical companies are prohibited from marketing medications for off-label uses in both the United States and Canada. In the United States, there have been several recent multi-billion dollar settlements with pharmaceutical companies based, partly, on off-label promotion. Health Canada has not publicized any investigations into, or prosecutions of, pharmaceutical companies for off-label promotion in Canada even though many of the same medications are marketed here. The prohibition on off-label promotion is largely directed at preventing pharmaceutical companies from circumventing the drug licensing process and attendant safety checks. OBJECTIVE: To determine if sanctions for off-label pharmaceutical promotion in one jurisdiction can be used to regulate marketing in another. METHODS: We reviewed and compared the laws and regulatory bodies in Canada and the United States to determine if Canadian regulators could use the findings of American regulators. RESULTS: There were no important differences in the laws and regulatory bodies in Canada and the United States related to off-label promotion. CONCLUSIONS: Canadian regulators can use the findings of American regulators to investigate off-label promotion in Canada. All countries should consider using sanctions in other jurisdictions to direct the deployment of limited regulatory resources.
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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 | 0.037 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".