Inductive Risk and OxyContin: The Ethics of Evidence and Post-Market Surveillance of Pharmaceuticals in Canada
Bibliographic record
Abstract
Abstract The argument from inductive risk claims that judgments about the moral severity of errors are relevant to decisions about what should count as sufficient evidence for accepting claims. While this idea has been explored in connection with evidence required for the approval of pharmaceuticals, the role of inductive risk in the post-approval process has been largely neglected. In this article, we examine the ethics of inductive risk in connection with revisions to the product monograph for OxyContin in Canada, which understates the risks of addiction and abuse associated with this drug. Using the concept of inductive risk, we consider what evidence should have been sufficient for Health Canada (HC) to revise the product monograph for OxyContin. Given the stakes involved, we argue that a less strict standard of evidence would have been appropriate, yet HC in fact took the opposite course, insisting upon a higher standard of evidence than it normally requires. In addition to providing a novel perspective on the opioid crisis in Canada, this article contributes to existing philosophical work by demonstrating that inductive risks in the post-approval stage are important and linked to pre-approval inductive risks.
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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 | 0.067 | 0.218 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.053 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".