The Oliveri Case: Lessons for Australasia
Bibliographic record
Abstract
The case of Dr. Nancy Olivieri, the Hospital for Sick Children, the University of Toronto, and Apotex Inc. vividly illustrates many of the issues central to contemporary health research and the safety of research participants. First, it exemplifies the financial and health stakes in such research. Second, it shows deficits in the ways in which research is governed. Finally, it was and remains relevant not only in Toronto but in communities across Canada and well beyond its borders because, absent appropriate policies, what happened in Toronto could have happened (and could well still happen) elsewhere. In Part One of this paper, we review the facts of the Olivieri case relevant to the issues we wish to highlight: first, the right of participants in a clinical trial to be informed of a risk that an investigator had identified during the course of the trial and the obligation of the investigator to inform participants (both her own and those of other investigators); and second, the obligation of institutions to protect and promote the well-being of research participants as well as academic freedom and research integrity, the obligations of research sponsors to inform participants, research regulators, and others about unforeseen risks, and the obligations of research regulators to ensure that participants are informed of unforeseen risks and to otherwise protect and promote research integrity. In Part Two, we relate these facts and issues to New Zealand and Australia. We also make detailed recommendations for changes to the various instruments used for the governance of research involving humans in Australasia.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".