Ethics of Pirated Drug Use and How do You Deal with it in Your Practice
Bibliographic record
Abstract
Over the past 13 years, I have examined some 150 ethical issues that arise in veterinary medicine, either directly in my Canadian Veterinary Journal column or in the ethics column I edit for the Veterinary Forum. While some of these issues represent true dilemmas, with strong arguments that can be marshalled on each side, many others are quite straightforward and their ethical resolution is unambiguous. I cannot think of a better example of an issue with clear ethical resolution than the one facing us in this discussion-the use of counterfeit (pirated) drugs where veterinarians dispense copies of established veterinary drugs. The basic reason this is a problem is that drugs compounded from bulk ingredients-unlike the branded (FDA approved) products or approved US generic equivalents-do not undergo FDA approved testing for safety and efficacy, and are not produced under conditions that conform to FDA's Good Manufacturing Practices. In my view, dispensing such drugs, as done by many veterinarians to increase profit well above what they can make with approved drugs, violates every sort of moral obligation inherent in veterinary medical ethics.
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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 | 0.054 | 0.195 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.020 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".